The group was primarily drawn from Code for Africa’s broad network, which was built through targeted mapping, continent-wide surveys and the MediaData database. In addition, snowball sampling was employed during the survey, which means that interviewees played an active role in defining who to include, and some participants may come from outside Code for Africa’s network. Among this set of South African indie info providers interviewed, we learned that:

Acknowledgments

Thank you to Code for Africa and Liz Kelly Nelson for input throughout this process; Nechama Brodie and Sarah Chiumbu for their thoughtful feedback on this report; Jonathon Berlin and Kurt Cunningham for web and graphic design; Grace Nuri for support with transcription and data processing; and Greta Alquist for editing this report. This project was made possible by the financial support of the Lenfest Institute and a second anonymous donor. We thank all the creators who participated in this report.

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Overview

As of September 2024, about one in five people in the United States get news from individuals rather than organizations. This trend is even more common among younger people. 

There is a growing mix of networks supporting what CNTI refers to in this report as “indie info providers” in the U.S. alone. To name just a few, Project C, our recruitment partner for this report, primarily serves former journalists building independent ventures; the Tiny News Collective brings together community members trying to meet their own community’s information needs; News Creator Corps trains creators from non-journalism backgrounds in media literacy; Listening Post Collective supports communities and community info providers; and the Evidence Collective supports health and science communicators with deep professional expertise. Many indie info providers are engaged with more than one of these organizations, emphasizing how diverse their professional backgrounds are — which makes it difficult to generalize about them.

CNTI sought to better understand this active arena of indie info providers playing increasingly important roles in people’s daily lives. What are their backgrounds, motivations, relationships with their audiences, revenue streams and strategies and their sense of their role in the broader news information landscape?  

To offer a starting point, CNTI conducted a series of in-depth 60- to 90-minute interviews with a mix of U.S. indie info providers, defined as “people who are working to provide verified factual information with a personality- or voice-driven brand that leverages the creator economy.” The group was drawn from within Project C’s broad network, with an emphasis on hearing from science communicators, subject-matter experts and civic-minded community members in addition to the largely journalistic base. (We have also conducted a similar set of interviews with these types of providers in South Africa, which we look forward to reporting on soon.) 

Among this set of U.S. indie info providers interviewed, we learned that they are: 

“[If I could do it all over again], maybe I could have thought [out] the roll-out in a little bit of a smarter way to try to beef up my subscribers before I started … maybe done a teaser campaign or been more shameless about promoting myself.“

Acknowledgments

CNTI thanks Liz Kelly Nelson for input throughout this process; Amy Kovac-Ashley, Celeste LeCompte, Afrooz Mosallaei and Ben Werdmuller for their thoughtful feedback on this report; Jonathon Berlin and Kurt Cunningham for web and graphic design; Grace Nuri for logistical support and additional feedback; Angelica Ruzanova for support with transcription and data processing; and Greta Alquist for editing this report. This project was made possible by the financial support of the Lenfest Institute and a second anonymous donor. We thank all the creators who participated in this report.

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AI governance is a complex ecosystem, incorporating policy instruments ranging from global compacts and legally binding domestic regulation to best practice standards and industry guidelines.¹ In December 2025, CNTI published a review of 188 governmental policy instruments and their impact on journalism, with a primary focus on legally binding legislation in various states of approval.²

In parallel, CNTI’s AI and Journalism Research Working Group reviewed the state of research on AI governance within newsrooms, including research on ethical implications and newsroom policy development for other emerging technologies. This briefing synthesizes 30 recent research papers.

Newsroom Policies Impacting AI in Journalism

Several studies show that while AI is being used in newsrooms, formal codification within newsrooms and professional societies is not yet universal, and there are still barriers to implementing AI policies.⁴ The working group reviewed 30 research articles addressing AI governance impacts on journalism, including policies developed by newsrooms and press associations, technology companies and governments. 

The speed of research is slower than the speed of policy development, which in turn lags behind technological development: it may well be that newsrooms have updated, added or advanced internal policies since these papers were published. Nonetheless, the takeaways and cautions from this research are still valuable, especially as journalists continue to develop policies.

Newsroom and professional guidance about the use of AI are not yet ubiquitous. As of late 2024, about 80% of the 221 Global South journalists surveyed by Thomson Reuters Foundation said their newsrooms have no AI policy.⁶ This number has almost certainly changed since then. However, what remains relevant is the barriers to developing and implementing policies identified here and in additional studies from around the world — including Germany, Greece, the Netherlands and Kenya.⁷ Barriers include a lack of access to technical expertise, difficulty in getting input and buy-in across organizations, the speed of technological change, the absence of regulatory frameworks in some places and the difficulty of complying with existing regulation in others. All the same, across contexts, journalists express the desire for guidelines and oversight. 

The newsrooms that do have AI policies share a similar approach, prioritizing transparency about the use of AI, human supervision of AI tools and human verification of outputs.⁸ However, few of these guidelines operationalize these priorities concretely or include clear oversight mechanisms. For example, some guidelines reference “proper” or “appropriate” uses without defining these terms. To date, three peer-reviewed papers explore newsroom policies, stylebooks and standards on the use of AI tools in journalism.⁹ Between them, these papers included 97 distinct policies from the European Union, Latin America as a region and 22 individual countries. 

These researchers also find that newsroom AI policies are ill equipped to address subtle biases that may be built into third-party tools. The guidelines focus more on AI outputs than on the systems themselves,¹⁰ are more concerned with generative than analytic AI¹¹ and rarely, if ever, provide practical guidance for working with third-party technologies.¹² For example, as outlined in the working group’s Transcription and Translation briefing, AI translation tools can introduce biases that may be difficult for non-experts to detect — like assuming doctors are men and nurses are women. These types of subtle biases exist beyond translation. By not addressing these concerns, newsroom AI policies fail to recognize that AI tools can harm journalistic integrity — and potentially journalistic independence — in ways that are difficult to detect. ¹³

In particular, few policies have clearly articulated when relying on third-party tools is appropriate and inappropriate. In theory, these concerns can be addressed through organizational procurement policies and guidelines that clearly identify the risks of different uses, particularly regarding data privacy and confidentiality. Concerns include the possibility that technology companies become indirectly involved in the newsroom, specifically, in the development of content.¹⁴ The major AI developers are primarily platform companies — including Google, Microsoft and Amazon¹⁵ — and newsrooms have long been at least somewhat dependent on them for distribution. Interviews with newsworkers suggest AI adoption is increasing dependence on platform companies, especially on the news production side.¹⁶ These concerns may be exacerbated in the Global South, because nearly all the early newsroom AI policies come from the Global North, while later policies borrow from them without necessarily addressing context-specific concerns,¹⁷ such as transcription quality issues. 

Procurement is an area where there has been little research to date. A 2025 study that considered 16 AI tools’ terms of service alongside interviews with decision-makers in newsrooms identified an ongoing challenge; most contracts granted developers the right to change the terms of service and the conditions without notice.¹⁸ This study also highlighted relative asymmetries between news organizations and AI developers as a barrier to managing risk contractually, a concern which may not even rise to awareness among individual journalists. While proprietary and local tools may have lower (or at least more customizable) risks than off-the-shelf ones, only a small number of the largest and wealthiest news organizations can practically build their own tools. (One promising recent conference paper explored how collaboratively governed and built LLMs could support the journalism field and address precisely this problem — but much more work is needed in this area.¹⁹)

Earlier guidelines addressing other new technologies in the newsroom — such as photo editing and social media — provide some useful parallels. As with AI policies, these policies typically start by articulating what journalism is and should be before highlighting how technology can support it and what uses are unacceptable. Like AI policies, photo policies did not always operationalize their values clearly; journalists and editors might disagree about what constitutes “excessive” retouching.²⁰

It is also common for social media policies to emphasize that journalists’ social media use must be consistent with existing journalistic values, ethics and procedures — including transparency and verification.²¹ Several researchers have also found that social media policies often protect news organizations — sometimes at the expense of individual journalists.²² Several studies of social media policies have found that differences in lived experience between editors developing the policies and reporters following them likely contributed to gaps in policies. In general, research analyses of various newsroom policies have concluded that including more stakeholders with varying job responsibilities and life experiences contributes to stronger policies.²³ A study that analyzed journalists’ tweets found that even when social media policies restrict their speech, journalists generally follow them.²⁴ Both the value of including more stakeholders and journalists’ general willingness to follow organizational policies are also likely to apply to AI policies.

Where More Research Would Be Helpful

• More research is needed on newsroom policies outside of the EU. There is some research, but much of it relies on data collected before the public release of ChatGPT and thus the widespread use of generative AI tools.
• There is also very little research on procurement, platform dependency, or relationships between newsrooms and technology companies outside the European context. 
• As technology and its use matures in news organizations, there is a need for more empirical and descriptive research, in addition to the early theoretical work.
• There is a lack of specific guidance for journalists and media organizations, especially regarding the use of third-party tools that may not be transparent. This is a particularly important gap since the research shows these tools may impact editorial decisions inconspicuously. 
• Given the distinct scopes, contexts and resources of different newsrooms, it is also important to examine how AI guidelines are being operationalized in daily workflows, as well as who participates in policy creation within the newsroom.

Footnotes

1. Ifayemi et al., 2024 ︎
2. Goodson et al., 2025 ︎
3. Mari, 2024 ︎
4. Porlezza 2024; Lefèvre et al., 2025 ︎
5. Lefèvre et al., 2025; van Drunen, 2025 ︎
6. Radcliffe, 2025 ︎
7. Dodds et al., 2025; Hofeditz et al., 2025; Kalfeli & Angeli, 2025; Mbaabu, 2025; Umejei et al., 2025 ︎
8. Becker et al., 2025; de-Lima-Santos et al., 2024; Sánchez-García et al., 2025 ︎
9. Becker et al., 2025; de-Lima-Santos et al., 2024; Sánchez-García et al., 2025, See also Cools & Diakopoulos, 2023 and Peck, 2023 for shorter pieces that have not been peer-reviewed. ︎
10. Becker et al., 2025, Porlezza, 2024 ︎
11. Sánchez-García et al., 2025 ︎
12. de-Lima-Santos et al., 2024; see also van Drunen, 2025 ︎
13. Becker et al., 2025; de-Lima-Santos et al., 2024; Sánchez-García et al., 2025 ︎
14. Simon, 2024; Simon, 2025; van Drunen, 2025. See also Timcke & Schroeder, 2025. ︎
15. Simon, 2022 ︎
16. Simon, 2024; see Seipp et al., 2024 for a policy perspective ︎
17. de-Lima-Santos et al., 2024 ︎
18. Piasecki & Helberger, 2025 ︎
19. Tseng et al., 2025 ︎
20. Solaroli, 2017 ︎
21. Duffy & Knight, 2019; Herrera-Damas 2014; Harlow, 2023; Sacco & Bossio, 2017 ︎
22. Harlow, 2023; Miller & Nelson, 2022; Molyneux & Nelson, 2024 ︎
23. Miller & Nelson, 2022; Molyneux & Nelson, 2024 ︎
24. Lu et al., 2025 ︎

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AI chatbots — such as ChatGPT, Gemini, Grok, Google AI Mode or the Washington Post’s “Ask the Post AI” — are software products which simulate human-like conversations and generate responses across a wide range of topics. While even their creators caution against using them as arbiters of fact, research consistently demonstrates that people increasingly rely on them for information about the world. Since the line between “information” and “news” is hardly clear-cut and information-seeking is also likely to include many topics where users of AI chatbots might previously have turned to news sources, news providers need an understanding of those broader informational habits.

This report provides a detailed snapshot of relatively early adopters and their use of AI chatbots to get information, including news. In October 2025, CNTI interviewed 53 people in India and the U.S. who use AI chatbots at least once a week and said they “keep informed about issues and events of the day” at least “somewhat closely.” 

Top-level findings:

CNTI found that most AI chatbot users currently use them to supplement their existing information repertoires, not to replace them entirely. They toggle back and forth between AI chatbots, news sites, search engines, official sources and more. We saw:

• Actionable information: Interviewees use AI chatbots to act on what’s happening and understand it, more than simply to know about it or to feel something about it.
• Personalization: AI chatbot users see these tools as fast, easy, personalized, customizable and friendly ways of getting information.
• Low understanding but higher trust: Few AI chatbot users have deep knowledge about the processes behind either journalism or AI chatbots. At the same time, interviewees express a general trust of AI chatbots alongside a general distrust of news media (outside of some users’ trust in their specific sources).

More specifically:

Interviewees use AI chatbots to act on what’s happening and to understand it, more than simply to know about it or to feel something about it.

What most drives AI chatbot use is the desire for information that helps people act. This is the most common use case for AI chatbots in both countries. Few interviewees want information for its own sake. Instead, they are looking to inform their choices and actions. AI chatbots stand out, especially in the U.S., for proactively helping people meet the need to act.

When it comes to knowledge for knowledge’s sake, interviewees in both countries use AI chatbots to supplement existing news habits, not replace them. No interviewee in either country said they rely solely on AI chatbots when they want to know what is happening. They do turn specifically to AI chatbots to learn what’s new about a story they are familiar with or to check a story that they don’t think is accurate (the latter now offered on some social media platforms, such as @grok on Twitter or Meta AI on Facebook).

Both U.S. and Indian AI chatbot users turn to them for context that is often absent in traditional news media. Many interviewees said that news stories often start in the middle, with background information buried or difficult to find. They find AI chatbots helpful for understanding who a public figure is, how another country’s legal system works and the history of Israeli-Palestinian relations — all contexts that news stories sometimes take for granted.

Emotional stimulation and entertainment are low information priorities. The “need to feel” does not seem to be a focus for our interviewees in either country, and did not come up much in our conversations. Interviewees do engage emotionally with AI chatbots, but only rarely with the information itself.

Interviewees see AI chatbots as a fast, easy, personalized, customizable and friendly way of getting information.

Few interviewees have deep knowledge about the processes behind either journalism or AI chatbots. At the same time, they expressed generally positive attitudes towards AI chatbots alongside generally negative ones towards news media.

Acknowledgements

The authors would like to thank Amy Mitchell for ongoing intellectual mentorship, partnership and leadership; Nupur Chowdhury, Rahul Dass, Kyong Mazzaro, Amy Mitchell and Nikita Roy for their thoughtful feedback on this report; Jonathon Berlin and Kurt Cunningham for web and graphic design; Angelica Ruzanova for support with transcription and data processing; and Greta Alquist for editing this report.

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Whether newsrooms are paying enough attention or not, however, many governments are adopting AI strategies, policies and laws. These regulatory attempts rarely directly address journalism and vary dramatically in their frameworks, enforcement capacity and level of international coordination, but they will almost certainly have an impact on journalism and the digital information environment. To understand exactly how regulation is, or could be, impacting journalism, we reviewed 188 national and regional AI strategies, laws and policies that collectively cover more than 99 countries. 

The analysis is structured around the seven regions of the world, as identified by the World Bank — North America, Latin America and the Caribbean, Europe and Central Asia, the Middle East and North Africa, Sub-Saharan Africa, East Asia and the Pacific, and South Asia — and around how regulatory activity in each of these regions addresses seven policy components that, based on the research of CNTI and others, are particularly relevant to and likely to impact journalism:

Explore the proposals

CNTI reviewed 188 national and regional AI strategies, laws and policies around the world to determine how they impact journalism. We focused our analysis around how regulatory activity addresses seven policy components that are particularly likely to impact journalism. Use the below map to explore each of the policy documents we reviewed and to see what journalism-related topics they include. Use the column on the left to see which documents mention a specific topic, or click on a country to see the list of its documents and the topics covered in each. 

Key findings: The landscape

The governance of AI is increasingly complex and varies dramatically by country and industry. 

The broad range of governance instruments in play include legally-binding regulation at every level of government, corporate policies, industry standards and many other types of documents. AI applications are being introduced across sectors: some of them, such as banking and health, are already heavily regulated, while others are much more lightly regulated. Even among national-level legislation, there is tremendous diversity in scope: some bills are sweeping and comprehensive, while others comprise small edits to existing laws or address AI in narrowly-defined use cases like online shopping, mammograms or road accidents. Others create new agencies, committees or councils to operationalize vaguely laid out principles. The law-making process varies by country, as do the stakeholders involved. There is also variability in countries’ power to impact technology companies. In some smaller markets, companies may consider pulling out rather than comply with regulations, especially if those regulations are burdensome or do not align with the requirements of larger markets. Furthermore, some countries — the United States among them — have dozens or even hundreds of bills in the pipeline, making it impossible to conduct an exhaustive review. While we offer numbers wherever possible to give a sense of scale and proportion, our research examines the breadth of policies, strategies and laws rather than providing a full audit.

As with all policy proposals, it is important to consider the AI documents we reviewed in their individual country contexts and in relation to pre-existing laws in those countries and regions. Even if journalism or data privacy is not explicitly mentioned in an AI document, for example, existing protections for these rights may supersede new AI proposals. It is also important for policymakers to coordinate with experts to determine whether their AI policy proposals are technologically feasible as they consider replicating the documents reviewed in this paper or originating new AI policy proposals. 

Policies don’t have to mention journalism by name to impact it. 

Twenty of the 188 documents explicitly mentioned “journalist,” “journalism,” “news,” “media” or “news media.”

When these terms do appear, they range from passing references to the field to laws whose primary concern is the practice of journalism. This by no means is a suggestion that more documents should directly name journalism. Calling out the journalism field in policy documents frequently has its own pitfalls: Once governments define “journalism” or “news,” those definitions can be weaponized against the news media. Instead, it is important that policymakers are aware of and think through potential impacts of directly naming or not naming journalism and news. 

• Four documents (from Bahrain, Chile, Costa Rica and Kenya) identify journalists or media workers as stakeholders, but go no further. Three others (from the African Union, Panama and Serbia) specifically identify the field of journalism as an important audience for educational materials and reskilling. 
• Four documents (from Algeria, Egypt, Lesotho and Sri Lanka) emphasize the importance of news media as a communications channel for public awareness campaigns, with varying assumptions about editorial independence from government. 
• One proposed law (from Ecuador) attempts to intervene in polarization and fragmentation by requiring providers of content recommendation algorithms to include content from a broad range of media outlets.
• Five different bills and laws exempt journalism from specific provisions: China’s Interim Measures on Generative AI say that other journalism laws supersede it; three U.S. bills would exempt news media from specific restrictions on deepfakes in the context of both reporting and advertisements (Illinois, New Hampshire and South Dakota); and Brazil’s proposed law grants exemptions to some forms of copyright violation for journalistic or research purposes..
• Two proposed laws — both from the U.S., one in New Jersey and one in New York — would specifically regulate the use of AI in journalism.

Transparency and data protection are the two topic areas (among the seven studied) that come up most frequently.

Of the 188 AI strategies, laws and policies we reviewed, 124 addressed transparency and accountability; 107 addressed data protection and privacy; 92 addressed algorithmic discrimination and bias; 76 addressed public information and awareness about AI; 64 addressed manipulated or synthetic content; 49 addressed intellectual property and copyright; and 19 addressed freedom of speech and expression. Each document could be counted in multiple categories. In all seven regions, either transparency or data protection was the most common topic. In every region, more than half of the documents we reviewed addressed transparency and accountability. Likewise, in every region, more than half of the documents we reviewed addressed privacy and data protection, with the exception of North America, which only featured the topic in eight of the 29 documents we examined. The emphasis on transparency likely responds to a key challenge for accountability: the opaqueness of many AI systems leaves even their creators without a full understanding of how they work, let alone policymakers. Meanwhile, the salience of data privacy is to be expected, since it has been a key issue in technology governance over the last ten years. At least four out of five people around the world are protected by a national-level privacy law. 

Freedom of speech and expression come up least often.

While policymakers regularly express concern about AI’s impacts on the information environment, references to freedom of speech and expression are infrequent across regions. In fact, these topics did not appear in any documents we reviewed from either the Middle East and North Africa or Sub-Saharan Africa. Moreover, the documents we reviewed addressed two very different concerns: Some, like Malaysia, addressed the possibility that using AI would threaten fundamental human freedoms, while others, like Venezuela, addressed the possibility that regulating AI would threaten human freedoms. The latter was more common in the United States than elsewhere, but both issues came up across regions. The EU’s AI Act highlights both concerns, emphasizing that AI systems can violate fundamental freedoms but also that labeling obligations do not restrict free speech. One resolution, New Jersey’s AR 158, instead focuses on freedom of speech about AI by urging technology companies to embrace stronger whistleblower protections. We note that the absence of explicit language about freedom of speech or expression does not necessarily indicate an absence of concern; in many countries, these freedoms are guaranteed in foundational legal documents, like constitutions, that supersede all other laws or policies. The certainty around the upholding of those foundational documents, however, cannot be guaranteed.

Key findings: The topics

Freedom of speech and expression

Out of the 188 documents we reviewed, 20 address freedom of speech and expression directly.

CNTI’s analysis finds that when freedom of speech and expression are recognized, it generally has positive implications for journalism. This demonstrates an understanding of the importance of this freedom and an acknowledgement of how AI can impact it. Because it is often only included in preambles or as a guiding principle, however, these policies often do not specify how they are going to protect these rights. Examining the range of existing levels of press freedom, both across and within regions, is foundational to understanding how these policies may be used.

Of the few concrete policies about freedom of speech and expression, most are likely to benefit journalism. Several countries ban AI systems that do not respect freedom of expression, but they do not specify how or what uses of AI systems would fail to respect fundamental freedom. For example, Argentina’s Bill 2573-S-2024 prohibits the use of AI “which violates fundamental human rights such as privacy, freedom of expression, equality or human dignity.” Other countries recognize trade-offs: In order to ensure that algorithmic content recommendation does not violate freedom of expression, Ecuador’s Organic Law for the Regulation & Promotion of AI in Ecuador requires clear terms and conditions, human supervision, accountability reports and an appeals process. These provisions would make it more difficult for journalists to be subject to censorship at the hands of an overly conservative algorithm. 

Manipulated or synthetic content

Of the 188 documents we reviewed, 64 address manipulated or synthetic content directly. At least two documents from each region contain provisions about this issue.

CNTI’s analysis finds that attempts to prevent the spread of false information and highlight the provenance of information are broadly positive for journalism and the information space — but these efforts must be scrutinized to ensure they do not infringe on freedom of expression. 

For example, some proposals may protect journalists from technology-facilitated gender-based violence and other forms of AI-generated harassment. When a journalist is targeted with a deepfake, it can push them into silence, decrease their credibility and even drive them to leave the field. By banning explicit deepfakes, the United States’ Take It Down Act, Mexico’s Ley Olimpia and Bahrain’s draft AI Regulation Law would prohibit such attacks on journalists. 

However, some of these proposals may end up penalizing journalists. For example, the United States’ 2025 Take It Down Act prohibits the nonconsensual publication of both authentic and AI-generated intimate images and requires online platforms to remove such images. It does not, however, include safeguards against bad-faith or fraudulent takedown requests, which some advocates say could be used to wrongly censor journalism. 

Moreover, some provisions could potentially be used by the government to target journalists for reporting it does not like. Bahrain’s draft AI Regulation Law would prohibit using AI to “upload or install personal images that damage an individual’s reputation or dignity; [… or …] modify, edit, or tamper with textual, audio, or visual content related to individuals without their explicit consent.” These provisions are sufficiently vague that they could potentially be weaponized against journalists for innocuous changes, like editing the levels of an audio recording to make it clearer.

Finally, some proposals could impact journalists’ ability to use AI to protect their sources. The Dominican Republic’s proposed Bill 563, for example, would ban the use of deepfakes to alter videos and punish violators with prison terms and hefty fines. Because it does not include an exception for journalism, even when it is labeled, this could prevent journalists from using AI for legitimate purposes, such as to create an avatar of a source who wishes to remain anonymous for their safety.

Many laws also treat synthetic audio, images and video differently than synthetic text, increasing complexity in this space.

Algorithmic discrimination and bias

Out of the 188 documents we reviewed, 92 address algorithmic discrimination and bias directly. 

Some laws would limit the tools that news organizations can use for decision-making, but these laws would not uniquely impact this sector — they typically focus on the use of AI tools for hiring and other consequential decisions, and would apply universally. News organizations would simply have to comply with these regulations.

However, policies that focus on bias in content recommendation systems could potentially have strong impacts on the reach of journalistic content. Content recommendation systems impact the content people see online, especially on social media platforms. Some news organizations also use them to suggest content to audiences. If social media platforms are required to recommend a diversity of sources and opinions, this would impact the reach of journalistic content, but it’s hard to tell if it will extend or limit that reach. Some AI policies — particularly in the EU — would limit the recommender tools used by social media platforms, but not by news organizations themselves, making the impacts unpredictable. Elsewhere, such as in Ecuador, provisions would require news organizations to scrutinize their personalization or content delivery tools, such as AI-powered, customizable homepages.

Other documents, such as those in Bangladesh and Lesotho, would push journalists and newsrooms to be more cognizant of biases in tools they use to analyze data or create content, especially if these provisions require representative or diverse training data. Because journalists already prioritize objectivity, such awareness falls under good journalistic practice and is unlikely to penalize journalists.

Looking beyond journalistic uses of AI, some proposals create registries and audits, which may facilitate journalists’ ability to conduct accountability reporting on AI more broadly. For example, Lesotho’s Draft Artificial Intelligence Policy and Implementation Plan calls on the country’s AI regulator and policymakers to develop “bias mitigation programs,” by mandating bias audits, providing open-source tools for bias mitigation and detection, and training developers and other stakeholders on bias prevention. This could grant journalists greater insight into how these technologies work, thus improving their reporting capability and increasing public accountability. It could also make it easier for them to select appropriate tools for their professional use.

Intellectual property and copyright

Out of the 188 documents we reviewed, 49 address intellectual property and copyright directly. At least three documents from each region included this policy component.

Copyright and intellectual property regulations are particularly complicated because different countries’ laws are incompatible with each other, and few have reckoned with major shifts in distribution made possible by the internet. 

We found that some bills and laws require licensing and compensation for the use of copyrighted material to train AI models. Colombia’s Bill 293 states that beyond an exception for scientific uses, developers cannot use copyrighted content to train AI without prior and explicit consent. On the other hand, the Digital Single Market Directive of the EU AI Act, for example, does allow copyright holders to ‘opt-out’ of their content being used as training data for commercial purposes. The bill in Colombia also gives copyright collectives the right to authorize, prohibit, or restrict the use of works under their management, and they can demand just and equitable remuneration in order to license that use. These legal provisions will likely have positive financial impacts for journalism producers if they can be enforced.

At the same time, a number of documents mention the importance of protecting existing copyright and intellectual property laws without necessarily specifying how AI systems — particularly those using data in training models — fit into these legal foundations, let alone offering a comprehensive assessment of the value of these interactions with digital content. 

For example, China’s generative AI regulation stipulates that both deployers and developers need to comply with intellectual property laws in the country, but it does not specify how AI systems and AI-generated content fit into those laws. 

Japan, on the other hand, takes one of the most permissive stances, allowing the use of copyrighted works for AI training regardless of purpose, so long as it does not unreasonably prejudice the rights-holder’s interests. These divergent approaches raise the issue of interoperability when models are trained globally and deployed across borders. 

The matter of intellectual property and AI is far from settled, though. In November 2025, a court in the United Kingdom found that AI models are subject to copyright infringement claims; however, the lawsuit did not answer the question of whether using copyrighted materials to train AI models falls within the U.K.’s “fair dealing” provisions. Courts in the United States have taken differing views, with two judges in California ruling that AI companies’ use of copyrighted materials to train LLMs constitutes “fair use” because the work is “transformative,” albeit with caveats and for different reasons, while a judge in Delaware ruled that a different AI company’s use of copyrighted materials was not “transformative” and, thus, did not meet “fair use.” This area will continue to evolve as ongoing lawsuits determine whether copyrighted material can be used to train AI models around the world, including in the U.S., India and Japan. 

What is becoming clear is the need to holistically address the value of the various kinds of uses of and interactions with digital content in building AI models and beyond. Current policy approaches tend to dilate between narrow licensing regimes and permissive exceptions, but few adequately balance creators’ rights, developers’ needs and the public interest, let alone the vast array of content itself. 

Transparency and accountability

Out of the 188 documents we reviewed, 124 address transparency and accountability directly. At least three documents from each region included this policy component.

CNTI’s analysis finds that their impact on journalism will likely vary depending on whether transparency obligations fall more heavily on developers or deployers. If they fall on developers, such as in Malaysia, it will be easier for journalists to assess whether third-party tools are valuable to their work; if they fall on deployers, like in this Argentine proposal, journalists, as deployers, would be held responsible for choices made by third-party companies which could lead to greater attentiveness among journalists, and, if not, to unanticipated lawsuits. Several documents, however, fall somewhere in between. The United Arab Emirates’ AI Ethics Guide, for example, states that “accountability for the outcomes of an AI system lies not with the system itself but is apportioned between those who design, develop and deploy it,” meaning journalists could be held accountable at any stage of the process.

We also found that, in general, transparency requirements can make it easier for journalists to report across a range of topics and sectors. For example, California’s AI Transparency Act requires transparency on the data used to train AI models, which would increase public understanding of the tools and, thus, allow journalists to better assess these models.

Some proposals would also require news organizations to label AI-generated content to ensure the public recognizes when it is being used. New York’s proposed Senate Bill S6748, for example, would require publications to “conspicuously” identify at the top of a page or webpage when AI is used to either partially or wholly create an article, image, video or other piece of content. Such requirements could potentially be a form of “compelled speech” or could reduce trust in the content. 

Data protection and privacy 

Out of the 188 documents we reviewed, 107 address data protection and privacy directly.

AI systems and tools are trained on vast quantities of data, and many countries have comprehensive privacy legislation in place that will interact with AI regulation in complex ways.

AI legislation, policies and strategies that address data protection and privacy typically do not consider the specific needs of journalists, who require access to sensitive data for investigative reporting. Bangladesh’s National Artificial Intelligence Policy, for example, states that “personal data usage will require valid consent, notice, and the option to revoke.” Requiring journalists to receive consent before using personal details, however, could inhibit investigative reporting on corruption, human rights abuses and more, given that the politicians, business leaders and others in question would likely not grant consent for their data to be used.

On the other hand, CNTI’s analysis finds that proposals and laws that ban the use of AI for surveillance, as some countries have contemplated, would likely improve the safety of journalists and their sources. For example, the EU AI Act bans AI systems that “create or expand facial recognition databases through the untargeted scraping of facial images from the internet or CCTV footage.” While this represents a step in the right direction, the law does have exemptions for national security. Some countries have taken advantage of this; Hungary, for example, allowed the police to use biometric surveillance to identify participants in LGBTQIA+ public events. 

Public information and awareness

Out of the 188 documents we reviewed, 76 address this issue directly. At least two documents from each region directly touch on this issue.

Many provisions for broad public awareness include journalists among their audiences, providing up-to-date sociotechnical knowledge that could inform stronger reporting. For example, Serbia’s AI strategy includes plans to organize seminars on AI, information security and big data specifically for journalists. Other policies offer similar information to all adults. 

Documents that recognize the importance of journalism as a vehicle for public awareness are varied in their strategies. Lesotho’s draft policy encourages stakeholders to do outreach with a diverse spectrum of media outlets, acknowledging the importance of news without raising concerns about press independence. On the other hand, Egypt’s strategy expressly calls on the media to share “positive news of AI,” perhaps suggesting a bid to influence coverage. No document we reviewed calls for governments to spend advertising dollars to place public awareness campaigns in news media.

Recommendations

The inclusion or exclusion of these seven topics does not necessarily make a proposal “bad” or “good” overall or for journalism and the digital information space. There are many important considerations for AI policy proposals, of which journalism is just one. It is one, though, that CNTI finds incredibly important to functioning societies.

If an independent, diverse news media and open internet are not protected in the AI era, these new regulations could potentially criminalize journalism, threaten news business models, contribute to information disorder and prevent the public from accessing a diversity of fact-based news. The solution is not to call out journalism by name in every AI proposal, as that can have unintended consequences for the field, but it is essential that policymakers see journalists as important stakeholders in AI discussions and consider journalism’s viability as they develop future proposals. Likewise, it is important that news organizations and journalists see themselves as key stakeholders and thoroughly engage in thinking through and discussing the future of AI regulation.

The analysis surfaces a few key areas in need of specific policy attention: 

1. 1. In AI proposals that address manipulated content, it is important that policymakers work towards methods that protect certain journalistic uses in ways that do not enable government censorship or determination of who is or is not a journalist. This is far from an easy task and may mean the best path is no legal policy at all. Either way, it is critical to fully think through. Legislative efforts to prevent the spread of disinformation can sometimes have unintended consequences for journalism. For example, legislation that regulates the provision and sharing of manipulated content but does not include exceptions for journalism can lead to journalists being targeted for allegedly “spreading disinformation” for reporting on the existence of false information, such as a deepfake of an elected official, for using AI-manipulated content (like AI-generated avatars or voice-altered audio) to protect a source, or for covering information that those in power do not want covered. While not legally feasible in the U.S., carve outs for journalists in other countries could protect an independent news media, as well as a source’s right to privacy and willingness to share. But such carve outs, if not crafted extremely carefully, could also easily lead to greater censorship and criminalization. This takes coordinated, thoughtful deliberation. Alongside these discussions, journalists and news organizations should determine how best to convey their use of AI to the public, whether through labeling, watermarking or something else. 

2. 2. Bias audits and transparency measures are best implemented before a tool is deployed. To date, bias audits and transparency measures have often been reactions to issues that arise after deployment rather than components required prior to public launch. While not all issues can be identified in advance, more can be done before a tool is deployed, especially as these systems and our understanding of them matures. Policy can help lay out foundational requirements for audits, as well as for transparency, around how they are conducted and how issues are addressed, particularly for technology companies whose algorithms can have a social impact (i.e. if the algorithm uses personal data and makes or influences decisions that may have a significant impact on society or an individual). 

3. 3. Policymakers should ensure that AI working groups include journalism producers, product teams and engineers, alongside AI technologists, researchers, civil society and other relevant stakeholders. Policymakers face the challenge of attempting to legislate a technology that is constantly evolving and has wide-reaching consequences. As such, it is important that they meet with diverse stakeholders to ensure that they properly understand the technology they are trying to govern, as well as the consequences that both the technology and potential legislation could have on society more broadly. It is also critical that all at the table, including journalism leaders, are fully read-in and share the goal of collaboration, as such cooperation leads to the most optimal societal solutions. Expanded regional and international collaboration would also be valuable as AI regulation is a global challenge with cross-border implications. 

Acknowledgements

The authors would like to thank Monica Attard, Charlie Beckett, Niamh Burns, Claudia Del Pozo, Mohamed Farahat, Megan Gray, Assane Gueye, Jhalak Kakkar, Ashkhen Kazaryan, Tanit Koch, Prabhat Mishra, Amy Mitchell and Daniela Rojas for their thoughtful feedback on this report. 

The authors also extend their gratitude to Greta Alquist for editing this report, Jonathon Berlin and Ryan Marx for designing the graphics and maps, Kurt Cunningham for creating the web design and the team at CETRA for their translation work. 

The post Journalism’s New Frontier: An Analysis of Global AI Policy Proposals and Their Impacts on Journalism appeared first on Center for News, Technology & Innovation.

CNTI’s AI and Journalism Research Working Group looked at 55 research studies and other articles from computer science, social science and linguistics disciplines to better understand how AI is shaping transcription and translation and what these developments mean for journalism. These studies include data representing a range of geographic contexts and languages.

Artificial intelligence (AI) systems are increasingly being used for transcription — the process of converting audio to written form — and translation — the process of converting content in a source language to a target language. Journalist-facing transcription tools like Otter and Trint launched nearly ten years ago,¹⁰ and automated translation has been available to everyday users — at least for a limited set of languages — since the release of Google Translate in 2006.¹¹ Advances in AI systems mean that Google Translate and similar programs continue to improve rapidly.¹² However, journalists around the world do not uniformly experience the benefits of AI technologies in assisting with transcription and/or translation. Inconsistent access to technology and varying availability of high-quality and verified training data remain major challenges.

Overall, AI models are improving. However, transcription tools remain most accurate for a relatively narrow range of standard American English dialects and accents,¹³ and translation tools remain most accurate for only a few language pairs.¹⁴ There are significant gaps in performance for “low-resource” languages (i.e., languages with relatively little textual data online that can be used to train AI models) as well as concerns about accuracy in those languages. It should be noted that “low-resource” languages include a number of languages spoken by hundreds of millions of people. In fact, English is the language of about 50% of online content; the largest proportion of online content that any other language represents is 6%.¹⁵ Thousands of languages make only a minuscule imprint on the internet.¹⁶ Some of the ongoing efforts to close the divide between “low resource” and “high resource” languages focus on creating and improving training data. For example, Nigerian start-up Goloka is working with Meta to collect data in five Nigerian languages¹⁷ as part of a larger Meta-UNESCO partnership.

AI translation and transcription are rapidly developing areas of research. Nearly all the research articles cited in this briefing are from the last five years, and machine translation and transcription are steadily improving. The working group expects to see continued progress in building AI tools for these uses, especially for languages that have not received as much attention. However, there is also evidence that unverified AI translations are creating misinformation on sites like Wikipedia, and those low-quality translations are being used to train the next generation AI translation tools, leading to worse performance.¹⁸

Journalism Use Cases

Journalists regularly use AI tools for transcription and translation to assist in the production of news content, generally with human oversight over the process.¹⁹ Journalists also use transcription tools, such as the Houston Chronicle’s Meeting Monitor, to share summaries of government meetings²⁰ with the public. Large-scale automated transcription and translation tools, including A European Perspective and Dubawa, are assisting journalists to report on topics that would otherwise be difficult and time consuming to monitor. A European Perspective consists of 10 broadcasters across nine European countries that exchange content using AI transcription and translation.²¹ The automatic transcription of audio-visual content and translation between languages encourages greater coverage of European news topics while also using editorial oversight to correct cultural or linguistic details in outputs. Dubawa, an AI fact-checking system in Ghana and Nigeria, was specifically trained using local dialects and accents. The tool transcribes radio broadcasts and checks for mis- and disinformation in several local languages.²² In a similar vein, Paraguayan news outlet El Surti is building a community-based Guaraní language dataset and AI tools.²³

The degree to which AI systems have been implemented for transcription and translation depends on newsroom resources and languages in use. Through a series of semi-structured interviews in South Africa, one study finds that AI tools are mostly being integrated in larger newsrooms and used to aid in public-facing translation, particularly at public media outlets with mandates to publish in multiple official languages.²⁴ Still, journalists — especially those in the Global South — are concerned about the utility of AI tools for transcription and translation, given reported challenges with accents and lower accuracy for local languages.²⁵

Newsrooms find that using AI tools for translation saves time but can result in inaccuracies, necessitating human review, especially in audience-facing content. One study found that AI translations of international news in Tanzania were mostly accurate, but about 13% of the sentences included mistranslations, minor ambiguities or inaccuracies that missed cultural details, such as incorrectly translating the English phrase “street food” word-for-word as “food of the road” in Kiswahili, rather than using an idiomatic phrase.²⁶ Similarly, a 2023 study benchmarking AI transcription and translation tools for journalists also found that they can save considerable time even with human review, but translations into English perform better than other languages.²⁷ In general, hybrid translation, in which machine translations are reviewed by human experts, is a promising approach.²⁸

Translation and transcription tools can also be used to personalize news content for different audiences. For example, publishers can add widgets to their websites or apps that would allow audiences to access an automatically generated transcript of video or audio content, or to access all content in the language of their choice. Publishers are increasingly interested in providing these features.²⁹ However, publisher interest is currently far outpacing audience interest: according to the 2025 Reuters Digital News Report, 65% of publishers were actively exploring AI translation content but only 24% of audiences said they were interested in using AI translation.³⁰ Similarly, 75% of publishers were exploring making audio available in text format (and vice versa), but only 15% of audiences expressed an interest. The reasons for these gaps are beyond the scope of this report, but researchers have suggested they may be linked to a lack of awareness of what the features might look like, or to a broader audience distrust of AI in journalism.³¹ However, audiences are more comfortable with AI translation than with many other newsroom uses of AI.³²

What Level of Accuracy is Good Enough?

One question that has not been addressed by research, is what level of accuracy is good enough? For example, is it appropriate to use AI tools if translation and transcription reach a certain level of accuracy? Even a tool with 95% accuracy may still miss crucial cultural and language nuances. Thus, human review and revision are necessary to ensure accuracy and appropriateness for most public-facing content. Future research examining the value of different strategies for improving accuracy and appropriateness, such as diversifying sources of training data, metadata, language-specific models or algorithms, can help determine how to address accuracy standards. However, research can only inform what is, at its core, a value judgment.

Technical Evaluations of AI Transcription and Translation

Models for these types of tasks are rapidly improving,³³ and translations between certain language pairs — like Spanish and English — generally perform well.³⁴ Researchers find that while AI tools cannot currently handle cultural details and ambiguity as well as human experts can, they are promising in a number of other contexts, such as (1) translations of medical terms from English to German³⁵ and (2) translations of legal documents across Arabic and English.³⁶ Researchers also find that AI translations from Indonesian to English often rival those of students in translation educational programs when it comes to implementing techniques like paraphrasing and structural transposition.³⁷ Meanwhile, AI transcription is being used to increase coverage of government meetings.³⁸ Summarizing these meetings may be a particularly fruitful use of AI transcription because participation follows a consistent structure and because figurative language and wordplay are rare. Progress in this field continues as research and development of novel techniques to build training datasets advances and as the development of specific translation and transcription models receive more attention.³⁹

While AI transcription and translation technologies are improving, recent research also highlights limitations and shortcomings of these AI tools⁴⁰ — including those particularly relevant to professional fields like journalism.⁴¹ These limitations include the tools’ (1) inability to fully handle language ambiguity and cultural nuance, (2) struggle to perform tasks at the level of human experts and (3) biases in outputs based on personal characteristics and attributes present in speech and text data. Evaluating the quality of these tools is challenging in itself: some metrics are overly simplistic, while more holistic methods for evaluating quality or accuracy are opaque and hard to interpret.⁴²

AI translation tends to focus on words rather than meaning, but languages have a great deal of typological variation and do not necessarily have parallel sentence structure. Moreover, word-level translation often focuses overly on referential meaning (i.e., what something is about) and lacks attention to indexical meaning (i.e., the social functions of language). Languages do not align one-to-one, with words carrying different formality and/or emotional meanings; AI tools may use the incorrect word to reflect the perspective of the speaker.⁴³ For example, professional speech is less formal in English than in Korean⁴⁴ or Japanese,⁴⁵ and a more literal translation into those two languages will often be socially inappropriate. The outputs these systems produce also change depending on how the translation is described through prompting, such as using specific requests to retain key themes from the source language versus merely asking for a translation into a given language.⁴⁶ 

There is also evidence that AI translation models produce results with gender biases⁴⁷ (though these biases are diminishing as models improve) by consistently assigning gender to professions (e.g., assuming doctors are men and nurses are women).⁴⁸ A review of 133 studies finds that much research on this topic treats gender bias as a purely technical or linguistic problem, rather than examining its social dimensions; these authors also note that most of the studies used machines rather than people to evaluate bias.⁴⁹ Given the social nature of bias, the authors raise concerns about this evaluation method. In practice, these studies suggest that journalistic content that is translated without careful review may inadvertently produce outputs that include biased pronouns, occupations or perspectives stemming from the AI tool’s training data.

AI transcription tools carry their own limitations, such as a tendency to add content that was never said by the source. This type of error appears more commonly when transcribing speech with longer gaps between words and phrases.⁵⁰ Strikingly, there are also critical deficiencies in these tools when transcribing audio from people who speak any form of English besides a fairly narrowly defined set of standard American accents, such as both World Englishes⁵¹ and African American Vernacular English.⁵² 

Overall, a recurring theme in the literature reviewed by the working group was that AI tools for transcription and translation of “low-resource” languages are severely lacking.⁵³ There are significant gaps between human-written and LLM outputs in languages other than English,⁵⁴ with journalists in the Global South reporting less confidence in these tools than those in the Global North.⁵⁵ Among “low-resource” languages, machine translation for signed languages lags even further behind spoken ones.⁵⁶ The most used signed language data sets are small and frequently rely on interpreted data, which is likely to include considerable interference from spoken languages.⁵⁷ It is not yet clear how best to represent signed languages computationally, nor how best to evaluate translation between signed and spoken languages.⁵⁸

Although there are many challenges for AI transcription and translation, greater attention is being focused on “low-resource” languages than before. Two prominent examples, Masakhane and Dataphyte, seek to improve natural language processing (NLP) research across Africa. Yet more needs to be done, including designing tools for local settings — particularly in locations that have thus far received less attention from technology companies and AI developers.

Where More Research Would Be Helpful

• Day-to-day use: The research does not yet include a deep understanding of where and when journalists are using AI tools for transcription and translation, nor does it explain where and when they would like to once they feel adequate tools are available. What specific issues are the journalists finding? How might these issues be addressed? 
• Good-enough accuracy: What level of accuracy is good enough for journalistic content?⁵⁹ For example, is it acceptable if AI translation tools achieve 95% accuracy when handling text between two languages? The incorrect 5% may provide essential cultural context for audiences in the target language. Research can help inform news organizations and journalism providers to decide what they are comfortable with as current tools are unable to achieve 100% accuracy in every context. Journalism-specific benchmarks for assessing AI transcription and translation tools are also worth developing to address industry-specific needs.
• Languages studied: The research on bias in machine translation has been limited to relatively few languages — with a bias toward written texts — and has focused primarily on translation into English.⁶⁰ It also primarily focuses on gender bias in isolation from other social identities and contexts. Expanding research in this area would help news workers make informed decisions about when AI translation is appropriate and when the risks are too high.
• Downstream effects: There is little research about the downstream effects that bias in the tools might have on journalism. For example, if journalists are working under time pressure, are they less likely to interview sources whose voice automated tools do not transcribe as well? Are journalists identifying and editing gender bias in translation tools, or is it impacting their reporting, their audiences’ understanding or both?
• Validated data in more languages: If the goal is to use large language models (LLMs) for transcription and translation, considerable attention and effort needs to be placed on building robust LLM training data in non-English and low-resource languages.⁶¹ We need more high-quality translation training data⁶² that has been validated in both the source and target languages. Further research should examine how to do this most effectively and efficiently to create inclusive AI models.
• Third-party tools: Larger news organizations are developing in-house AI models, particularly adaptations of smaller language models that can be run entirely locally.⁶³ Meanwhile, many smaller newsrooms are relying on pre-existing models from technology developers. More research needs to consider the potential impacts of relying on third-party tools,⁶⁴ and explore how smaller newsrooms can (1) adapt (fine-tune) existing models to better fit their needs and/or (2) build custom AI tools that are financially viable for them.

Footnotes

1. Mari, 2024 ︎
2. Ananny & Pearce, 2025; Beckett & Yaseen, 2023; Gondwe, 2025; Kahn, 2025; Munoriyarwa et al., 2023; Simon & Isaza-Ibarra, 2023 ︎
3. Qingliang; 2024; Shahmerdanova, 2025; Tokalac, 2023 ︎
4. Spencer, 2025 ︎
5. Court & Elsner, 2024; Kocmi et al., 2024; Kocmi et al. 2025; Pava et al., 2025; Moghe et al., 2025 ︎
6. Chan et al., 2022; Chang et al., 2024 ︎
7. Ghosh & Caliskan, 2023; Prates et al., 2020; Savoldi et al., 2025; Ullmann, 2022 ︎
8. Fredrikzon, 2025 ︎
9. Dubois et al., 2024 ︎
10. Jarnow, 2017 ︎
11. Frey & Llanos-Paredes, 2025 ︎
12. Caswell & Liang, 2020 ︎
13. Chan et al., 2022; Chang et al., 2024 ︎
14. Court & Elsner, 2024; Kocmi et al., 2024; Kocmi et al. 2025 ︎
15. W3Techs, n.d. ︎
16. Brandom, 2023 ︎
17. Orimemi, 2025 ︎
18. Judah, 2025 ︎
19. Ananny & Pearce, 2025; Beckett & Yaseen, 2023; Simon & Isaza-Ibarra, 2023; Canavilhas, 2022; Ohumu, 2025; Simon, 2025 ︎
20. Langer, 2025 ︎
21. Canavilhas, 2022 ︎
22. Ohumu, 2025. See Vo 2025 for another example. ︎
23. Valdez Sanabria & Auyanet, 2025 ︎
24. Munoriyarwa et al., 2023 ︎
25. Beckett & Yaseen, 2023; Munoriyarwa et al., 2023; Kahn, 2025 ︎
26. Gondwe, 2025 ︎
27. Tokalac, 2023 ︎
28. Shahmerdanova, 2025 ︎
29. Newman & Cherubini, 2025; Ross Arguedas, 2025 ︎
30. Ross Arguedas, 2025 ︎
31. Ross Arguedas, 2025 ︎
32. Simon et al., 2025 ︎
33. Breaking Language Barriers with AI: Maximizing Accuracy and Efficiency with Machine Translation Technology, 2023 ︎
34. Alonso Jiménez & Rosado, 2024 ︎
35. Noll et al., 2025 ︎
36. Moneus & Sahari, 2024 ︎
37. Asi et al., 2024 ︎
38. Langer, 2025; Schellmann, 2025 ︎
39. Guo et al., 2025; Kocmi et al., 2024; Kocmi et al. 2025; Levit et al., 2017; Moghe et al. 2025; Wang, 2022 ︎
40. Lee, 2024; Novytska et al., 2025; Yan et al., 2024 ︎
41. Schellmann, 2025; Song, 2020 ︎
42. Leiter et al., 2024 ︎
43. Song, 2020 ︎
44. Song, 2020 ︎
45. Lee, 2024 ︎
46. Lee, 2024 ︎
47. Savoldi et al., 2025; Ullmann, 2022 ︎
48. Ghosh & Caliskan, 2023; Prates et al., 2020 ︎
49. Savoldi et al., 2025 ︎
50. Koenecke et al., 2024; Schellmann, 2025 ︎
51. Chan et al., 2022 ︎
52. Chang et al., 2024 ︎
53. Ojewale et al., 2025; Ojo et al., 2025; Pava et al., 2025 ︎
54. Guo et al., 2025 ︎
55. Beckett & Yaseen, 2023 ︎
56. Wolfe et al., 2025 ︎
57. De Coster et al., 2024 ︎
58. De Coster et al., 2024 ︎
59. How best to evaluate translation is largely out of scope, but see Leiter 2024. ︎
60. Savoldi et al., 2025 ︎
61. Howcroft & Gkatzia, 2022 ︎
62. Qingliang, 2024 ︎
63. Hagar et al., 2025 ︎
64. Simon, 2024 ︎

The post AI Transcription and Translation in Journalism appeared first on Center for News, Technology & Innovation.

This is the first in a series of reports from the AI and Journalism Research Working Group convened by the Center for News, Technology & Innovation (CNTI). The working group currently consists of more than 15 cross-industry members from around the world, bringing research, journalism and technology experience to the discussions. Each quarter, we’ll synthesize the state of global research across two or three questions or topics at the intersection of journalism and AI.

The goal of the working group is to summarize research for an audience of journalism practitioners and researchers around the world. That means we focus on what’s actionable for journalism, not other fields that are concerned with AI.

What do we mean by “AI”?

This report uses the OECD definition: “An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.”

Wherever possible, we try to use specific terms rather than “AI” to avoid conflation or confusion. Journalism has been adopting forms of automation for more than 50 years,¹ but widespread use of the term “AI” is more recent — and may include both newer technologies and those that have been in use for quite some time.

The Focus of This Report

This briefing focuses broadly on journalistic communication about AI. We first summarize research and frameworks that address the following questions about societal AI literacy:

• What does the public currently know about AI, and what does the public need to know about AI?

We then move on to a synthesis of current research on journalists’ communication about AI in two different contexts:

• How should journalists and news organizations communicate about journalistic uses of AI?
• How do journalists report on AI and automation more broadly, and what are the consequences for their audiences?

In each section, we lay out the general findings of the research to date, any suggested considerations or actions for practitioners that emerge and areas where more or new research is needed. A clear takeaway across the board is that journalists and news organizations need to communicate more clearly about journalism itself — not only about AI.

This report was prepared by the research and professional staff of CNTI in partnership with several external contributors who collectively authored this briefing.

If you have ideas or research findings that are important for CNTI and the working group to include, please email them to info@cnti.org.

What the public knows, and needs to know, about AI

There is increasing consensus across fields that the public requires more knowledge about AI — including about different types of systems, what these systems can and can’t do and how to critically assess their output.² Despite the publication of several systematic reviews, there is not yet a consensus on exactly what so-called “AI literacy” should include, other than that it should cover both computational and ethical dimensions.³ Nor is it clear how to include “AI literacy” in broader conceptions of media and information literacy.

While the field of journalism can’t be expected to address AI literacy alone, news reports are one of the primary ways that adults learn about new technologies.⁴ As a consensus begins to emerge about what people should know, the role of journalism in AI literacy may also become clearer.

Global Perspectives

The way AI development, use and literacy are playing out in the Global North and the Global South varies widely. What journalists know and need to know — before we even consider the broader public — simply does not look the same in different political and economic contexts with variable infrastructure and educational systems. 

Working group member Oluwapelumi Oginni shares her perspective about AI and journalism in Africa:

“While AI adoption in journalism is becoming a well-explored topic in the Global North, the conversation in the Global South still requires more exploration, especially within the context of the real on-the-ground barriers around skills and resources. The discussion is not just about whether journalists want to use AI, but whether they can. Effective use of AI in newsrooms depends heavily on two things: first, journalists and editors need to understand what these technologies can do (AI literacy), and second, they need the financial capacity to implement them. In many African countries, and indeed, in the Global South, that second part is a serious roadblock…⁵

[W]hile the idea of AI in journalism is gaining ground, the infrastructure, funding, and training needed to make it work at scale are still very much missing.⁶ What this means, ultimately, is that journalists and media houses in the Global South, particularly in Africa, are operating at a double disadvantage. Though they are expected to keep pace with global innovation cycles, many lack the financial means and technical training needed to truly compete or drive innovation. This makes it all the more important for research to explore, in depth, the uneven realities of media systems operating in low-resource environments.”

The education field offers a popular framework⁷ that breaks down AI literacy into four components: 

1. Literacy practices (the ability to understand and evaluate AI, which people can demonstrate directly) 
2. Core values (which support learners to use the tools safely and effectively) 
3. Modes of engagement with the tools (understanding, evaluating and using AI tools) 
4. Types of use (the purposes for which learners use AI tools) 

Under this framework, journalism may be well suited to help adults build literacy practices (rather than core values or specific types of use). Literacy practices include holistic and fundamental knowledge that crosses social and technical domains. However, it is unlikely that journalism can — or should — directly teach adults how to use AI tools.

Within the realm of literacy practices, some concepts are more fundamental than others. A 2024 synthesis⁸ highlights three key facets that differentiate AI literacy practices from other technological literacies:

1. First, AI is (relatively) autonomous. It can act without human intervention and sometimes have material impacts on the world without humans knowing it was used. 
2. Second, AI can learn. Its input data can enable automated improvement.
3. Third, AI is “inscrutable” – deeply challenging to understand or interpret. AI inference is not analogous to human reasoning. For the most part, its models and decision-making processes are opaque or “black-boxed.” They may only be understandable to a point, or only to people with advanced technical knowledge. That, in turn, can also make it hard to interpret outputs.

The authors of this synthesis also note that AI presents two additional qualities that violate long-held assumptions of human-technology interaction: 

1. AI has inconsistent outputs. Because models are increasingly probabilistic rather than deterministic, the same input will not always lead to the same output. For example, a calculator will give the same results to the same query every time; an algorithmic search engine may not. And ongoing changes and improvements to models may lead to inconsistencies over time.
2. It is opaque. Because AI is “under the hood” of so many technologies and has no specific interface, users may not even know when they’re interacting with it.

These five aspects of AI sit at the intersection of understanding and awareness, where journalism is best poised to intervene. For example, these high-level points could be addressed through evergreen explainers and sidebars that outlets reuse across stories on AI.

The field of journalism may also be well positioned to help people understand how content delivery algorithms work. Understanding these algorithms can help people increase their agency over information, inform citizenship and strengthen community building, among other outcomes.⁹

Research we reviewed suggests

It is important for anyone explaining AI to focus on the humans behind AI systems and remind audiences that computers and humans have different and complementary strengths.¹⁰ In particular, “AI is a tool that uses existing data to make predictions or generate content. In other words, it is creating a best approximation … based on what has already been created by humans.”¹¹

Aspects of AI literacy that may be most relevant for journalism

Journalism plays a part in supporting adults in learning about new technologies, including “AI literacy” and “algorithmic literacy.” It is important that the approach to coverage fully realizes and thinks holistically about this role.

Any given outlet’s audience will have different background knowledge and different information needs. Journalists and outlets will need to ensure they understand their particular audience, rather than developing a universal blueprint to explain emerging technologies.

Journalism may be well suited to connect the dots between technical explanations and social and ethical impacts,¹² since they frequently draw similar connections in their work. On the other hand, journalists may not be as well positioned to train the public in how to use AI tools, since experiential learning is likely to be more effective.¹³ 

Journalists are also in a strong place to report on funding, business models, government relationships and other political factors behind the development of AI and other new technologies. 

Where more research would would be helpful

Before research can help journalism support public AI literacy, we need better answers to three big questions:

• What do people need to know about AI? (Relatedly, researchers should also consider what people currently know about AI so that efforts can meet the public where they are at this time.)
• What can journalism realistically teach people about AI?
• What are the impacts current reporting on AI has on AI literacy?

Frameworks and validated measures for AI literacy — which will likely come from educators and psychometricians — would make it easier to measure the efficacy of particular reporting strategies on public AI literacy.¹⁴

Communicating About AI Use In Journalism

“Transparency” and “disclosure” are buzzwords in the AI and journalism space these days: dozens of toolkits and guidelines advise news organizations on their importance vis-à-vis the use of AI, and how to best communicate about its use. But there is much less agreement on the goals behind being transparent about AI use (including where and when it is needed), the value to audiences and what works best to improve audience understanding and awareness. Members of the public consistently say that they want transparency about AI use,¹⁵ but current data offers, at best, a mixed sense of what that actually means in practice. Given both the range of use cases for journalism and the need for better public understanding of both AI and journalism, we need more data on how the public uses and interprets disclosures in context. 

Global Perspectives

Several surveys of journalists suggest that Global South journalists are highly likely to adopt AI tools and other new technologies.¹⁶ One recent survey found that more than eight in ten Global South journalists say they use AI for work, but less than one in five say their organization has policies about AI use.¹⁷ 

Working group member Zara Schroeder shares her perspective: 

This gap highlights a pressing need for institutional guidance, training, and ethical frameworks to support responsible adoption.¹⁸ Without such policies, journalists may inadvertently compromise accuracy, transparency or privacy, especially in high-stakes reporting environments.¹⁹ 

Moreover, disparities in access to resources, infrastructure and AI literacy between Global South and Global North newsrooms may exacerbate existing inequalities in news production and distribution.²⁰ Journalists in under-resourced contexts may rely on freely available generative AI tools without fully understanding their limitations, biases or risks of misinformation. ²¹

For example, in countries like Zimbabwe, Uganda, Kenya and South Africa journalists are using AI tools to automate time-consuming tasks such as generating basic news reports, transcribing interviews or summarizing press releases.²² Off-the-shelf tools like ChatGPT, Otter.ai (for transcription) and Google’s AI-based-speech-to-text services are being used to speed up workflows, especially in fast-paced newsrooms with limited staff.²³

There is also a lot that the Global North can learn from homegrown African AI projects.

Journalists in Ghana and Nigeria have used machine learning models to help analyze large public datasets, such as government budgets, procurement records or COVID-19 case numbers to identify patterns, anomalies or potential corruption.²⁴ AI-driven data analysis tools help uncover insights that would be difficult to detect manually, enabling journalists to produce in-depth, evidence-based stories. 

Dataphyte, a Nigerian media and data analytics organization, launched Nubia, an open-source AI platform in 2022 designed to analyze large datasets and generate first-draft news stories.²⁵ Users input raw data (e.g. government statistics), and Nubia produces narrative reports, templates, data breakdowns and visuals which journalists then refine and contextualize. This significantly speeds up complex data-driven reporting. 

Another example is Dubawa, a West African fact-checking initiative, which has developed two AI-powered services; one is a WhatsApp chatbot that responds to user queries by drawing on previously verified fact-checks from Dubawa and the International Fact-Checking Network.²⁶ The second AI service is an audio platform that monitors live radio broadcasts, transcribes audio in Nigerian and Ghanaian English dialects, identifies suspicious claims and helps journalists verify or debunk them.²⁷ 

Overall trust in journalism and in the broader information environment also varies widely by region, as does knowledge about journalism. That makes it particularly difficult to generalize from studies that focus on a small number of countries. Local political pressures, media ownership structures and digital access all influence how AI tools are used and received by both journalists and audiences. 

To build a more comprehensive understanding, future research should incorporate regionally diverse case studies and prioritise collaborations with local media organizations. This can help ensure that the global AI transition in journalism is inclusive, context-sensitive and responsive to the needs of journalists working in a wide range of environments. 

In assessing this area of research, we considered over 20 studies related to AI transparency and disclosure, the full list of which can be found in the References section. Importantly, the discussion here focuses on transparency within the journalism industry, rather than transparency on platforms (e.g., Apple News) and social media sites (e.g., TikTok). There has been considerable research on transparency, especially compared with other questions related to AI and journalism, allowing for a more conclusive set of takeaways. However, it is important to note that the analytical methods, geographical ranges and specific research questions vary widely, from surveys and experiments to in-depth interviews with journalists or news audiences. 

A number of studies have explored whether labelling could further harm societal trust in information, but the results have varied with even small differences in label text. For example, one experiment found that labeling content “AI-generated” reduces audience ratings of its accuracy but not their trust in news or journalists.²⁸ Another found that a longer label specifying autonomous AI reduces perceived trustworthiness but not accuracy.²⁹ A third study suggests that some of these impacts may decrease as people become more familiar with these technologies.³⁰ Moreover, negative consequences of disclosure could align with other structured forms of bias. One recent study found that while both humans and LLMs rated identical news articles less favorably when an AI disclosure was present, the LLMs also penalized authors from particular racial or gender groups disproportionately for their transparency.³¹

Several studies (using different methods) find that audiences tend to overestimate the prevalence and autonomy of AI in journalism.³² Additional studies find audiences do not consistently or accurately interpret the intended meaning of AI attribution in bylines (e.g. “written by staff writer with artificial intelligence (AI) tool”) or simple labels.³³ Several studies also suggest that people with deeper knowledge about journalism have more concerns about AI use, perhaps because they better understand verification practices.³⁴ But offering excessive information can have negative impacts; while there is not much research on this aspect of AI transparency, a number of studies on privacy disclosure policies have explored “privacy fatigue” and disengagement.³⁵

Almost across the board, the research we reviewed suggests that many people lack a nuanced understanding of journalistic processes and principles. And recent data CNTI collected shows that journalists themselves recognize the need to do a better job communicating the field’s value in general.³⁶ It is nearly impossible to envision successful communication about technology tools that does not reckon with this larger challenge. After all, a major goal of journalism is to provide the public with reliable information in order to support civic participation, public life and democracy. And the norms of production that seek to make journalism reliable, such as independence and verification, also underlie journalists’ use of new technologies — these norms need to be better communicated to achieve greater levels of public trust in information. 

A 2024 synthesis paper argues that briefly explaining both human and machine contributions has shown some promise.³⁷ A recent meta-analysis found that people interpret AI-labeled content as slightly less credible but equally readable, accurate and fair.³⁸ Perceived AI authorship was also found to have a bigger impact than actual AI authorship on most measures³⁹ — all the more reason to provide information about the role of human journalists, highlighting the importance of verification.

Research we reviewed suggests

Before communicating about uses of AI, journalism outlets should consider communicating about the methods and value of journalism more broadly. 

Some methods, both human and technological, may best be explained as policies on a stand-alone page rather than repeated with every individual story or segment. For example, some frequent internal AI uses — like transcription and spellcheck — are widely accepted by audiences and may not require constant disclosure.⁴⁰

Rather than using the label “AI,” which many people interpret narrowly to refer only to generative AI, it may be clearer to answer the following three questions: What tool did I use? What did I use it to do? Do I stand by my work? In other words, it’s better to explain briefly how AI was used than to label that AI was used.⁴¹

At least at this point in time, the process of generating or manipulating images and video should be explained at every use, because research shows there is distrust and concern about these uses.⁴² However, what that explanation should contain remains less clear. Providing sources for AI-generated text may help mitigate distrust.⁴³

Journalism organizations should collaborate to build consensus around how to best operationalize shared values such as transparency and verification.⁴⁴

Where more research would be helpful

There has been far more research on transparency about AI use in synthetic text and images than audio — what information would be most appropriate?

How substantive must changes to photos or video be to require explanation? Many types of photo editing have been a common practice for years and are not typically explained. Existing photo manipulation policies, many of which date back to the widespread adoption of software such as Adobe Photoshop, may be helpful in developing guidelines and thresholds for communication.

How do people interact with transparency information in the context of existing news habits and routines?

What is the impact of transparency strategies on people’s understanding of AI? …on people’s understanding of journalism?

What is the relationship between reactions to transparency and (mis)trust in particular outlets and journalists?

Covering AI in Journalism

Journalism serves an important and powerful role in explaining how new technologies work to the public, impacting people’s understanding of and attitudes towards technology.⁴⁵ Recent developments in AI technologies have sparked research interest in coverage of AI, especially (1) when and how coverage has increased over time and (2) what sentiment and frames journalists are using to explain AI.⁴⁶ 

Global Perspectives

Of the topics explored in this briefing, the research about journalism’s coverage of AI is probably the most geographically diverse. Even so, considerations around issues like sourcing are likely to vary considerably by region. For example, journalists in countries with fewer technology companies may have less access to company spokespeople — but better access to outsourced workers. Infrastructure and regulatory contexts also vary in ways that may impact what’s most relevant to a particular audience.

Working group member Zara Schroeder shares examples:

In many African countries, journalists often report on the downstream effects of global tech decisions rather than direct interactions with Big Tech firms. This shapes how sources are selected and what stories get told. 

For instance, Ghanaian journalists have investigated the local impact of global content moderation work outsourced by companies like Facebook.⁴⁷ In 2023, Ghanaian outlets helped amplify stories from Nairobi, Kenya, where whistleblowers exposed poor working conditions and psychological harm among content moderators employed by third-party firms on behalf of Meta.⁴⁸ Without direct access to Silicon Valley executives and spokespeople, reporters in Ghana and across the region instead sourced their stories from affected workers, labor rights advocates and leaked internal communications demonstrating how sourcing adapts when direct access is limited. 

In Nigeria, journalists have covered growing concerns over AI-powered surveillance technologies, such as facial recognition systems used by law enforcement.⁴⁹ With little transparency from tech vendors or government bodies, reporters often rely on investigative methods like freedom of information requests, leaked documents or interviews with civil society watchdogs.⁵⁰ A notable case involved uncovering the quiet deployment of Chinese-made surveillance systems in public infrastructure. Here, sourcing is shaped more by investigative persistence and NGO collaboration than corporate access. 

These examples demonstrate that sourcing practices are nuanced globally, and that concerns about overreliance on sources that are not independent may be less salient in lower-access environments. Instead, sourcing in these environments is often more grassroots, labor-intensive and dependent on alternative networks of information. Understanding these differences is critical for accurately interpreting journalism produced in diverse media ecosystems. 

We looked at two dozen studies which examine (1) the tone of AI coverage, (2) the language journalists use to frame AI (and potential benefits and risks), (3) interviews with journalists and (4) surveys and experiments. These studies reveal important findings but they are rarely conclusive due to gaps in the types of news organizations studied (large vs. small), the geographic contexts included and the unique research methods used. Thus, we need more data and research to form a comprehensive understanding of AI coverage in contexts around the world. 

When covering AI topics — including technology developments and regulations — several researchers find that coverage may not include a full range of sources and perspectives. This can pose challenges for covering AI because journalists may (1) rely too heavily on sources that are have a financial interest in the outcomes,⁵¹ (2) lack the technical expertise needed to verify claims or ask important questions of first-person reports⁵² and thus (3) be overly swayed by the positions or frames offered by their sources. Research also finds that coverage of AI follows one of two overarching narratives:⁵³ (1) AI-in-general, which orients towards expected future implications, treats AI as inevitable and foregrounds economic competition;⁵⁴ and (2) AI-in-particular, which emphasizes specific, current uses of the technology, foregrounds impacts to particular communities and stakeholders, and highlights both continuity and change. That is, stories that focus on concrete technologies tend to take a more measured and balanced approach.

Researchers have also explored whether coverage of AI portrays the technology positively or negatively (or both, depending on topic). These studies examine the sentiment and/or frames used to explain the technology to audiences. Findings to date suggest that the topic of the story matters a great deal as to whether the take is positive or negative.⁵⁵ For example, coverage of AI may lean positive regarding healthcare, economic topics and innovation,⁵⁶ neutral-to-negative in the context of political topics⁵⁷ and negative when discussing topics like data bias and cyber crime.⁵⁸ As AI systems continue to develop, it is important to know both how journalists are explaining these developments and also how the topic of the story impacts how journalists cover it.. 

The differences described above are likely due to unique research methods and settings. Researchers are exploring how AI is covered (1) in different countries (i.e., information environments), (2) by different news organizations, (3) across different time periods (e.g., 2010-2021, 2021-2024, etc.), (4) with different measures for sentiment, topics and framing and (5) using unique keywords (e.g., “artificial intelligence” and “big data” or “artificial intelligence,” “robots/robotics,” “algorithms” and “automation”) to select articles for inclusion in the study. 

Another factor that can shape AI coverage is the ideological make-up of a news organization’s audience or overt ideology of the news organization itself. Indeed, one study found that organizations with either left or right ideological biases (based on ratings from Media Bias/Fact Check,⁵⁹ an independent effort in which fact checkers grade organizations) report on AI risks more than center/low ideological bias organizations.⁶⁰ Yet, it remains to be seen if these findings will be corroborated by further research.

Most studies consistently find that newsroom coverage of AI dramatically increased during the mid-2010s.⁶¹ Much of this research, however, does not include coverage after late 2022, when ChatGPT was released to the public. Recent analyses, though, find steady increases in the number of articles about AI through 2023.⁶² Coverage of AI remains a major area of focus in newsrooms and forthcoming research will shed light on how the amount of coverage has changed over time.

Research to date has explored a range of research questions — including the tone of AI coverage, the frames used to explain AI technologies and differences in AI coverage over time — but research does not necessarily yield clear assessments of the quality or thoroughness of AI coverage (especially outside of English-speaking locations). While there are some valuable learnings from the research to date, it is critical that we (1) have more research and (2) consistently apply that research to understanding AI coverage at a deeper level.

Research we reviewed suggests

Journalism should continue to track not just how much they cover AI but how they are covering it. To do so, newsrooms need to have a deep understanding of the technology itself and the range of stakeholders involved. Journalists can then ask deeper questions, seek input from a range of voices and monitor their own framing more effectively. It is critical that they are intentional and aware of the frames they use because the public learns about technologies from this coverage.⁶³

Coverage of AI should consistently include a comprehensive selection of perspectives, including affected workers, cross-industry experts, non-industry technical experts, members of the general public and critics — in addition to technology company and government sources — to ensure the public has access to a full range of information.⁶⁴

News organizations should prioritize coverage of specific, current uses, which can help audiences form a clearer understanding of what the technologies can and can’t do. However, broad, future-focused reporting that treats the technology as inevitable may lead to hype and unrealistic expectations of both benefits and harms.⁶⁵

As AI technologies continue to develop and improve, journalists and newsrooms should continue to clarify that AI systems (1) are built and maintained by humans and (2) do not have the ability to think, understand, talk, etc. the way humans do.⁶⁶

Where more research would be helpful

We need to continue building knowledge — across a variety of settings — about how specific topics, sources and perspectives relate to how AI is covered. While there has been a lot of research in this area, there are few conclusive findings that can be applied globally. 

Learning how audiences around the world react to different coverage language and frames — likely through experimental designs⁶⁷ — is important for understanding how coverage affects audiences’ beliefs about AI. 

Most research has examined national-level news coverage, whereas regional and local news organizations have received much less attention. How does coverage of AI differ when looking at local and regional news organizations?

There are also gaps when it comes to geographic location. Research to date has focused on the U.S. and Western Europe. Does coverage elsewhere show similar patterns? What is unique to each region of the world?

Similarly, most of the research uses English-language (Anglophone) news coverage. Further expanding research to non-English sources will provide a more comprehensive understanding of AI coverage.

What constitutes ‘comprehensive’ or ‘high-quality’ reporting on technologies and their political and economic contexts?

Current Working Group Members

A list of current working group members and their affiliations is shown here:

• Akintunde Babatunde
  Executive Director, Centre for Journalism Innovation and Development
• Jay Barchas-Lichtenstein
  Senior Research Manager, Center for News, Technology & Innovation
• Madhav Chinnappa
  Independent Media Consultant
• Utsav Gandhi
  Research Intern, Center for News, Technology & Innovation; PhD Student, University of Illinois Chicago
• Samuel Jens
  Research Associate, Center for News, Technology & Innovation
• Amy Mitchell
  Executive Director, Center for News, Technology & Innovation
• Sophie Morosoli
  Postdoctoral Researcher at the AI, Media & Democracy Lab, University of Amsterdam
• Gary Mundy
  Director Research, Policy and Impact, Thomson Foundation
• Oluwapelumi Oginni
  Project Manager, AI Initiatives, Centre for Journalism Innovation and Development
• Joshua Olufemi
  Executive Director, Dataphyte Foundation
• Amy Ross Arguedas
  Research Fellow at the Reuters Institute for the Study of Journalism
• Zara Schroeder
  Researcher, Research ICT Africa
• Felix M. Simon
  Research Fellow in AI and News, Reuters Institute for the Study of Journalism & Research Associate, Oxford Internet Institute, University of Oxford
• Scott Timcke
  Senior Research Associate, Research ICT Africa
• Jaemark Tordecilla
  Independent Media Advisor, Philippines

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Appendix

The studies and their research methods for the “Communicating about AI use in journalism” section are presented below. 

The studies for the “Covering AI in journalism” section, including time frames and data sources, are provided in the table below.

Footnotes

1. Mari, 2024 ︎
2. Biagini, 2025; Frau-Meigs, 2024; Gagrčin et al., 2024; Kumar & Sangwan, 2024; Mills et al., 2024; Oeldorf-Hirsch & Neubaum, 2025; Pinski & Benlian, 2024; Schüller, 2022; Tadimalla & Maher, 2024 ︎
3. Biagini, 2025; Gagrčin et al., 2024; Mills et al., 2024; Pinski & Benlian, 2024 ︎
4. Anderson et al., 2012; Takahashi & Tandoc, 2016; Yang et al., 2023 ︎
5. Jamil, 2021; Malik, 2025; Munoriyarwa et al., 2023 ︎
6. Adefioye, 2024; Ishengoma & Magolanga, 2025; Mukasa, 2024; Umeora, 2025  ︎
7. Mills et al., 2024 ︎
8. Pinski & Benlian, 2024; see also Berente et al., 2021 ︎
9. Gagrčin et al., 2024 ︎
10. Associated Press, 2024; Mills et al., 2024 ︎
11. Ruiz & Glazer, 2004 ︎
12. Biagini, 2025 ︎
13. Pinski & Benlian, 2024 ︎
14. Montag et al., 2024 ︎
15. Beckett & Yaseen, 2023; Fletcher & Kleis Nielsen, 2024; Gondwe, 2025; LeCompte et al., 2024; Piasecki et al., 2024; Ross Arguedas, 2024; Toff & Simon, 2024 ︎
16. Barchas-Lichtenstein et al., 2025; Radcliffe, 2025 ︎
17.  Radcliffe, 2025 ︎
18. Ncube et al., 2025 ︎
19. Radcliffe, 2025 ︎
20. Munoriyarwa, 2024 ︎
21. Olanipekun & Olakoyenikan, 2022 ︎
22. Alayande & Olufemi, 2023; Sofiullahi, 2024 ︎
23. Mugadzaweta, 2025; Lush, 2022; Mukasa, 2024; Hokkanen, 2025 ︎
24. Egwu & Saint, 2024 ︎
25. Egwu & Saint, 2024 ︎
26. Egwu & Saint, 2024 ︎
27. Egwu & Saint, 2024 ︎
28. Altay & Gilardi, 2024 ︎
29. Toff & Simon, 2024 ︎
30. Wang et al., 2025 ︎
31. Cheong et al., 2025 ︎
32. Altay & Gilardi, 2024; Fletcher & Kleis Nielson, 2024; Ross Arguedas, 2024 ︎
33. Bien-Aimé et al., 2025; Jia et al., 2024 ︎
34. Mattis et al., 2025; Toff & Simon, 2024 ︎
35. Lyu et al., 2024; van der Schyff et al., 2023; Zhu & Zhang, 2024 ︎
36. Barchas-Lichtenstein et al., 2025 ︎
37. Schell, 2024 ︎
38. Wang & Huang, 2024 ︎
39. Gilardi et al., 2025; Jia et al., 2024 ︎
40. Fletcher & Kleis Nielsen, 2024; Mitchell et al., 2025 ︎
41. Bien-Aimé et al., 2025; Jia et al., 2024; Wang & Huang, 2024 ︎
42. Fletcher & Kleis Nielsen, 2024; Mitchell et al., 2025; Thomson et al., 2024 ︎
43. Toff & Simon, 2024 ︎
44. Misri et al., n.d.; Morosoli et al., n.d.; Sánchez García et al., 2025 ︎
45. Anderson et al., 2012; Takahashi & Tandoc, 2016; Yang et al., 2023 ︎
46. Annany, 2024; Brause et al., 2023; Choi, 2024; Moran & Shaikh, 2022; Parratt-Fernández et al., 2024 ︎
47. Hall & Wilmot, 2025 ︎
48. Hall & Wilmot, 2025b; Höppner, 2025 ︎
49. Aikulola, 2025; Institute of Development Studies, 2023 ︎
50. The Cable, 2023a; 2023b; Kabir & Adebajo, 2023 ︎
51. Bunz & Braghieri, 2022; Canavilhas & Essenfelder, 2022 ︎
52. Brennen et al. 2022; Ji et al., 2024; Kuai, 2025 ︎
53. Lammar et al., 2025 ︎
54. See also Magalhães & Smit, 2025 ︎
55. Brantner & Saurwein, 2021; Canavilhas & Essenfelder, 2022 ︎
56. Mohammed et al., 2024; Moriniello et al., 2024 ︎
57. Canavilhas & Essenfelder, 2022 ︎
58. Nguyen & Hekman, 2024 ︎
59. Media Bias/Fact Check, 2025 ︎
60. Allaham et al., 2025 ︎
61. Brantner & Saurwein, 2021; Cools et al., 2022; Korneeva et al., 2023; Nguyen, 2023; Nguyen & Hekman, 2024; Valderrama et al., 2025 ︎
62. Bartholomew & Mehta, 2023; Valderrama et al., 2025 ︎
63. Allaham et al., 2025; Brantner & Saurwein, 2001; Choi, 2024; Korneeva et al., 2023; Nguyen, 2023; Nguyen & Hekman, 2024; Parratt-Fernández et al., 2024; Radcliffe, 2025 ︎
64. Ananny, 2024; Brennen et al., 2022; Canavilhas & Essenfelder, 2022; Mohammed et al., 2024 ︎
65. Lammar et al., 2025; Magalhães & Smit, 2025 ︎
66. Associated Press, 2024 ︎
67. Epstein et al., 2023 ︎

The post AI Literacy & Communication appeared first on Center for News, Technology & Innovation.

This report is the first in a series of deep dives into the data from the Center for News, Technology & Innovation (CNTI)’s global surveys of journalists and the public.

In conjunction with our partners at the Centre for Journalism Innovation and Development (CJID), CNTI explored what the surveys reveal about artificial intelligence (AI)’s impact on journalism in Africa. We focused on two central questions:

1. How do journalists in Africa feel about the relationship between journalism and technology, including AI?
2. How does the South African public feel about the relationship between journalism and technology, including AI?

The emergence, continuous evolution and daily use of AI presents both opportunities and challenges for the news industry. As newsrooms adapt to new technological developments and habits, they must also stay true to journalism’s mission and fundamentals. While the rise of AI-powered tools is revolutionizing storytelling, fact-checking, security and audience engagement, the tools’ associated ethical dilemmas, increased accessibility of spyware and government surveillance that use AI and rampant misinformation are persistent issues. 

To answer our two questions, we pull from two separate studies which reflect distinct pools of people. Our global survey of journalists included more than 100 journalists across Africa. Our public survey included a representative sample of the South African public. In both studies, we asked about attitudes towards and uses of technology as a whole — including several questions specifically focused on AI. As with all CNTI research, this report was prepared by the research and professional staff of CNTI.

Part I: What Journalists in Africa Think about Technology

Between October 14, 2024 and December 1, 2024, CNTI surveyed 433 journalists from 63 different countries. This report focuses on respondents based in Africa, including 75 in Nigeria and 34 in other African countries (N = 109). 

This data finds journalists in Africa to be more optimistic than journalists in the rest of the world about the impacts of technological developments — including AI and social media — on enabling journalists’ work and an informed public. They are also more likely to have incorporated technological tools into their work to do things like summarize or analyze documents and enhance their writing. Compared to journalists in other regions, journalists in Africa are more likely to report that their news organizations are transparent with audiences about the use of technology, especially when it is used to check accuracy or improve their writing. Still, similar to journalists around the world, they believe news organizations should pay as much attention as they currently do — or more — to the opportunities and risks AI presents to the news industry.

Journalists in Africa are overwhelmingly using digital technology for information gathering, communicating with sources and reaching their audiences

Similar to their global counterparts, African journalists highlighted the importance of digital technology for reaching their audiences (96%), information gathering (90%) and communicating with sources (89%).

Journalists in Africa say their news organizations are paying at least some attention to tech issues, but a plurality want to see them pay more attention to AI

Journalists in Africa (28%) are more likely to say that relationships with technology companies are getting a great deal of attention from their organization (compared with 18% of journalists in the rest of the globe). They report AI in journalism is getting a similar amount of attention as do journalists elsewhere.

Journalists in Africa rank both of these topics near the bottom for how much attention they get in newsrooms, surpassing only “Online abuse, surveillance, and cyber threats.”

About half of journalists in Africa think their news organization pays too little attention to developing ways AI can help journalists and other newsroom employees do their work and to mitigating the ways it can harm their ability to do their work (49 and 54%, respectively).These responses are similar to those of journalists in other regions of the world; when we look at all journalists together, those numbers are 45 and 48% respectively.

Journalists in Africa are more positive than others about the impacts of social media and developments in technology and AI

Journalists in Africa are more positive than journalists elsewhere about the impact of both “developments in technology” and “social media” on their ability to deliver journalism. Compared with 69% and 64% of journalists overall — and thus even smaller numbers of journalists on other continents — 87% and 77% of journalists in Africa  were positive about developments in technology and social media, respectively.

Journalists in Africa are also more positive about AI’s effects on enabling an informed public about events and issues of the day: 55% of African journalists said they were positive versus 36% of all journalists (and fewer still on other continents).

Journalists in Africa are more likely than others to have used technology in a variety of ways in their profession

African journalists were more likely than journalists elsewhere to have used technology to make their writing better (74% vs. 56% overall), summarize or analyze a lot of documents or data (75% vs. 60% overall), check the accuracy of something (69% vs. 52% overall), develop story drafts (54% vs. 38% overall) and help determine how to best present a news story to the public (67% vs. 48% overall).

African journalists were similar to their global counterparts in their likelihood to use technology to edit an image (52% vs. 45% overall), search archives of their reporting or their organization’s (59% vs. 51% overall) and translate content from one language to another (both 65%). 

Journalists in Africa think their organizations are doing a fairly good job of communicating about how they use technology — and they are more likely than journalists elsewhere to say their organization communicates about specific technology uses

Journalists in Africa agree with journalists elsewhere on how well their news organization communicates different uses of technology — including AI — to the public. A plurality say they are doing a fairly good job.

Journalists in Africa were more likely than journalists elsewhere to say their organization usually communicates the use of technology to their audience when making their writing better (83% vs. 66% overall), checking the accuracy of something (91% vs. 77% overall), developing story drafts (80% vs. 66% overall) and helping determine the best way to present a news story to the public (76% vs. 63% overall). 

Journalists in Africa were similar to their global counterparts when saying their organization communicates the use of technology to their audience when searching archives of their reporting or their organizations (82% vs. 71%), summarizing or analyzing a lot of documents or data (80% vs. 69%), translating content from one language to another (78% vs. 68%) and editing an image (75% vs. 70%). 

Journalists in Africa have had their voice, image (or both) reproduced by technology at rates similar to journalists across the rest of the globe

In the past 12 months, 11% of respondents in Africa learned of someone using technology to reproduce their voice (compared to 10% of all respondents) and 17% of someone using technology to reproduce their image (compared to 15% of all respondents).

Conclusion

Journalists in Africa stand out with their optimism about the impact of technological developments — including AI and social media — on their ability to produce journalism and enable an informed public. They also report using these technologies to assist them in a more diverse range of tasks than their global counterparts and their news organizations are more likely to report this use of technology to news consumers. 

At the same time, journalists in Africa share many common experiences and concerns with journalists in the rest of the world. Like journalists around the world, they rate the importance of technology to their work as extremely high. They experience technology being used to replicate their voice or their image at similar rates. And both the African and overall respondents express a desire for their news organizations to pay at least as much attention as they currently do — or more — to the opportunities and challenges of AI. 

Overall, this research highlights that while AI is impacting newsrooms everywhere, journalists in Africa are actively embracing these tools and are notably positive about the role of technology in the future of journalism.

Part II: What the South African Public Thinks About Technology

We surveyed 1,012 South African adults between September 23, 2024 and October 16, 2024 to understand their attitudes about journalistic uses of technologies such as AI. 

The data shows that the South African public views digital technology as very important and believes that future developments will help them keep informed. Further, they are using technology for content related tasks and express openness to journalists doing the same. Generally, they are overwhelmingly more optimistic about the future of the open internet than people from Australia, Brazil and the U.S.

When asked more specifically about AI, the South African public was relatively positive about AI’s future impact, not only on journalists’ ability to report but also on their own ability to stay informed. However, they are split on how much they care about journalists’ use of AI in reporting and they use multiple factors to decide if its use is okay. Even though there is a general sense of optimism about the future of AI and journalism, South Africans have heard less about AI than people elsewhere and they are using generative AI at low rates.

The South African public feels more positive about their ability to keep informed than people in Australia, Brazil and the U.S.

About two-thirds of South Africans (67%) feel positive about their ability to keep informed.

Of South Africans that consume 0-4 pieces of journalism per week, 32% say they keep up very closely; however, among people in other countries who consume the same amount of news, less than 10% say the same (6% Australia, 7% Brazil and 9% U.S.). 

The South African public says digital technology is very important for keeping informed and is positive about its future impact

A strong majority of South Africans say that digital technology is important for keeping informed, with 84% saying it is very important and 9% somewhat important.

About three-quarters of people in South Africa (74%) are optimistic about the impact developments in technology will have on their ability to keep informed and 16% are neutral.

Although a strong majority of the South African public is positive about technology, they report more challenges with it than people in Australia or the U.S. Over half (56%) say that having technology that works is at least somewhat of a challenge in their ability to stay informed.

The South African public uses technology for content-related tasks and is comfortable with journalists doing the same

Strong majorities of South Africans report using technology “to check the accuracy of something” (67%) and “to make their writing better” (66%).

The South African public is generally comfortable with journalists using technology, with three-quarters or more reporting acceptance of each use asked about.

The South African public is much more optimistic about an open internet than Australia, Brazil or the U.S.

In South Africa, eight in ten (79%) are at least somewhat confident the internet will be a place to get and share news openly in the future.

The South African public is relatively positive about AI’s future impact on both journalists’ ability to report and their ability to stay informed

Strong majorities of people in South Africa (71%) think AI will either have a positive impact on their ability to keep informed or will have no effect one way or another.

Roughly half of people in South Africa are optimistic (49%) and about one-in-four are neutral (22%).

We also asked people to consider whether AI will have a mostly positive, negative or neutral impact on “journalists’ ability to report on issues and events.” We found that a plurality of South Africans express a sense of optimism, with 46% saying they think it will mostly have a positive impact.

The South African public is split on how much they care about AI use in reporting

About one-in-three (32%) in South Africa feel it matters a great deal and about half (52%) say it matters at least a fair amount if AI is used in the reporting process.

The South African public considers multiple factors when deciding if a journalists’ use of AI is okay

We asked about five factors people might use to decide if a journalist’s use of AI is okay. Slim majorities of people in South Africa say that each factor is at least somewhat important (55-62%).

The South African public has not heard as much about AI as people in Australia, Brazil or the U.S., and most of them have not tried to use generative AI

 In South Africa, 22% say they have heard a great deal about recent developments in AI, and 17% said they have heard a fair amount — a total of 39%. Majorities in all other countries have heard at least a fair amount (62-73%).

About a quarter of people in South Africa have tried to use generative AI (27%) in the last year while the same proportion (27%) do not know what it is. 

Conclusion

Our data shows that the South African public recognizes the growing significance of technology in the news landscape and is generally optimistic about its potential, including advancements in AI. Despite this, there is a gap between the importance placed on technology for staying informed and the challenges in having devices that work. Further, while the South African public uses technology and expresses comfort with journalists also using it, compared to people elsewhere they may be less aware of and experienced with recent innovations, including generative AI.

Overall, the data suggests that while the South African public values technology’s role in news consumption, there is a knowledge and usage gap between South Africa and other countries.

Part III: Larger Trends

The clearest common thread across both the South African public and the journalists in Africa is optimism about technology. Both of CNTI’s surveys showed a similar optimism across the Global South, in comparison with the Global North. 

Research focused on the Global South — and Africa in particular — adds further detail about this optimism. The Thomson Reuters Foundation’s 2024 survey of 221 journalists across the Global South found that about 80% of respondents report using AI tools regularly in their work and about half were doing so daily. A 2023 Africa-specific study from International Media Support showed uneven AI adoption across Africa’s public interest media: while Kenyan and South African media outlets were leading in implementation, overall adoption across the continent was relatively low. 

Challenges to implementation across Africa include concerns about transparency, accountability and ethical safeguards — particularly in the absence of official guidelines. Among the participants in the TRF study, 13% said their organizations had official policies about AI use. And most of them were navigating this space independently, as they were largely self-taught, learning by “playing with tools” or using “online courses or guides.”

The 2023 International Media Support study found similar barriers, including a gap in knowledge about AI, resource constraints and concern about algorithmic bias and harm. And a 2023 interview study of 17 journalists from five African countries who had used ChatGPT in their work by January 2023 (soon after its release) revealed an increasing skepticism at generative AI’s early stages. These journalists generally found that outputs were not particularly sophisticated or up-to-date in their contexts. While they saw potential for AI, they also saw serious limitations, especially in localized contexts where the availability of training data may be limited. Similarly, a piece on AI in Kenyan newsrooms highlighted that much of the AI technology is developed from the West, and often trained on data that reflects a global perspective that may not align with Kenya’s local context.

Turning to the African public, there is also a notable sense of cautious optimism about the use of AI in media — with variation across the continent. Both Gregory Gondwe’s 2025 study of 1,960 members of the public from 10 African countries (Burkina Faso, Cameroon, the Democratic Republic of Congo, Ghana, Kenya, Malawi, Nigeria, Uganda, Zambia and Zimbabwe) and Mphathisi Ndlovu’s 2024 study on public perceptions of an AI-powered newsreader in Zimbabwe found that while there is moderate trust in AI-generated news overall, it varies widely. Gondwe’s study found significant differences in trust based on age and also explored the role media polarization and exposure to AI-generated content plays in this variation. Ndlovu found that while some audience members liked the use of AI news anchors, others were critical due to their lack of human emotion and accents. Ndlovu highlighted the need for “decolonizing the AI tools in newsrooms,” calling for greater sensitivity to local cultures and languages to increase acceptance across diverse audience groups. 

Overall, studies on African journalists and the public show that implementation has been uneven across the continent. Widespread challenges include lack of policies, knowledge gaps and issues of AI transparency — both in newsrooms explaining its use to consumers and model developers offering insight into the data used to train these systems. From the audiences’ perspective, this transparency is a very important consideration: in general, as found in Gondwe’s study, those who thought newsroom transparency about AI use was important also had somewhat higher trust in AI-generated content.

Last but not least, there is a lot of promising work happening around transparency and localization right now, including AI initiatives being developed by, with and for African journalists and newsrooms. These tools include fact-checking, transcription and data analysis applications.

About These Studies

Like many parts of the world, newsrooms in Africa are harnessing the potential of AI in their work. However, there are numerous advantages and disadvantages to this quickly evolving technology. Together with the Centre for Journalism Innovation and Development (CJID), the Center for News, Technology and Innovation (CNTI), wanted to explore the perspectives of African journalists’ when it comes to the challenges and opportunities of AI in the news industry. 

This project is part of CNTI’s broader Defining News Initiative which seeks to understand how journalism is defined today. Access to information is not just important for its own sake; it makes democracy possible. In 2024, the United Nations outlined Global Principles for Information Integrity in response to growing challenges around misinformation, disinformation and hate speech. 

The journalist data in this article are a subset from a larger global survey, What It Means to Do Journalism in the Age of AI: Journalist Views on Safety, Technology and Government. The representative data of South Africans are from a series of country-level surveys, What the Public Wants from Journalism in the Age of AI: A Four County Survey.

How We Recruited Participants

Journalist survey: Surveys are a snapshot of what people think at a particular moment in time. CNTI collected responses from 433 journalists across 63 countries between October 14 and December 1, 2024.This data highlights the perspective of journalists around the world during that time frame.

CNTI partnered with journalism organizations in multiple continents. The questionnaire was crafted with input from partner organizations who knew the current situations and challenges of their members across various country contexts. These partner organizations also shared the survey with their membership and can be found here. 

The data representing Africa in this article reflects the responses of journalists across the continent, with 75 in Nigeria and another 34 in other African countries (N = 109). The overall data reflects the responses of 433 journalists across 63 countries with 256 of our respondents (59%) coming from three countries: Mexico, Nigeria, and the U.S. Because no global census of journalists exists, no survey can be “fully representative” of all journalists.

South African public survey: Data are from an Infinite Insight RDD CATI/cell phone sample conducted from September 23 to October 16, 2024. The total sample size was 1,012 respondents. The design effect was 1.48 and a margin of error of 3.7 points. The survey was available in English (n = 811), Zulu (n = 138), Sesotho (n = 24), Sepedi (n = 17), Setswana (n = 12) and Xhosa (n = 10).¹ All interviews were conducted by telephone, and the median interview length was 19 minutes and 17 seconds. There were 47 interviewers all of whom were trained. The sample collection age categories were: 18-24, 25-34, 35-49, 50-64 and 65+. This survey was part of a larger four-country project. 

For specific questions about the sample frames, weighting procedures and/or additional survey details, please send an email to the research team at info@innovating.news.

How We Addressed Attrition

Journalist survey: The survey was lengthy (with an estimated completion time of 20 minutes) and consisted of five distinct sections on different topics. The survey was broken into sections, and we treated each as a drop-off point: that is, if a respondent answered at least one question within a section, non-responses were treated as true non-responses. Respondents who answered no questions within a section were not included within the section’s N.

For transparency, topline tables include both percentages of the full survey N (Percent) as well as percentages of the section N (Valid Percent).

South African public survey: We recoded missing, refused and don’t know responses into a catch-all category to keep the sample sizes consistent across each question that did not explicitly have survey logic (i.e., questions that were asked to every respondent).

How We Tested for Statistical Significance

Journalist survey: This report presents findings for both (1) the overall set of journalists and (2) journalists who reported living in Africa. Thus, the African journalists are nested within the overall results. To test for statistical significance, we compared responses from African journalists with those from non-African journalists to ensure independence between the groups. Responses were compared using Chi-squared proportion tests. We used a standard threshold of p < 0.05 for assessing statistical significance. Differences mentioned in the report text are statistically significant.

South African public survey: We analyzed the results using Chi-squared proportion tests to assess differences in responses between countries. We used a standard threshold of p < 0.05 for assessing statistical significance. Differences mentioned in the report text are statistically significant.

How We Protected Our Data

CNTI did not collect any identifiable information that risked the privacy and confidentiality of participants. For the survey of journalists, data collection was supervised by CNTI staff only. The survey included individual-level information such as gender and the country where one worked and resided. It would be very difficult to identify study participants because CNTI did not collect their personal contact information or contact participants directly and the participants’ personal information was not shared by the partner organizations.

For the survey in South Africa, Infinite Insight, the country vendor, handled data collection. Demographics were collected for weighting purposes. CNTI did not receive specific locations of or contact information for survey participants.

The data are securely stored in an encrypted folder which is only authorized to the core research team at CNTI.
More information about how each survey was administered may be found here for the journalist survey and here for the public survey in South Africa.

Footnotes

1. These numbers are unweighted. ︎

The post A Window into AI and Journalism in Africa: Perspectives from Journalists and the South African Public appeared first on Center for News, Technology & Innovation.

A lot of ink has been spilled over surveys that examine consumers’ news habits and the platforms they use to access news. Over the past few decades, research has consistently shown a steady shift toward digital platforms and away from print. Regularly conducted surveys have charted the rise (and fall) of various social media platforms. Television, particularly at the local level, has remained an important part of many consumers’ news diets, but even that has started to show decline — especially among young audiences — in some of the most recent such research.

Less attention has been paid to the more nuanced ways people interact with news and the range of content and sources they access across platforms, despite dramatic changes in these areas. In a recent set of focus groups, CNTI set out to better understand how the public defines news, journalism and journalists as well as what role they — and technology — play in keeping individuals informed about important issues and events. Unlike many studies on news habits, we opted for a more open-ended exploration of how people get informed about issues and events by avoiding explicitly asking people about “news” at the outset of conversations.

Our first two essays explored how people understand what news and journalism are, who can (and does) create that work, and how they view the role of generative AI in creating news. In this essay, we look more closely at how participants engage with this news ecosystem, describing the extensive set of habits and practices they have adopted in order to navigate this changing landscape.

We found, across countries and demographic groups, that participants focused much more on describing the habits and methods that they use to stay informed than they were on the sources themselves. Far from the “passive” news landscape that is often described in news habit research, our focus groups participants revealed active approaches to making sense of the mounting volume of multi-platform content. 

While they rely on access to an always-on environment in which information is “pushed” to them, participants see themselves as playing an important role both in curating the information that they regularly consume and in digging deeper into news to verify facts, deepen their understanding, and add perspectives.

Encountering Information Everywhere

The explosion of digital technologies has increased participants’ access to news and information throughout their days. Participants described getting information about topics of interest from a variety of sources: conversations with friends, family and colleagues; posts on social media feeds; messages in WhatsApp groups; and notifications or suggested content from news aggregator apps and other online portals. Sometimes, they seek out news and information from websites and mobile apps from specific news brands. Many keep radio and television programs playing in the background during their daily activities:

“It’s all at your fingertips. You can have as much or as little as you want.” (Australia)

“You know, at times, something is happening at that very moment. And then, you’re just browsing Instagram, and then a piece of news comes across, you know. It’s around you.” (Brazil, from Portuguese)

In all, participants describe daily lives awash in news and information. This has created a new set of challenges for individuals as they look to keep up with information and events. 

Many participants described a kind of constant scanning of the information around them, sometimes followed by a deeper dive into content that captures their attention:

I prefer to follow a variety of different topics, mostly health, pop culture, current events going on [in] the world, sports, a little bit of politics and such. I don’t usually actively seek out this information, unless a headline catches my eye and then I will do research to get more information on the topic. And I honestly don’t really have any key sources.” (United States)

More intentional news consumption, as described by these participants, usually happens during a moment of calm in their day or week: on a coffee break during the day, when babies are sleeping, after the chores are done, after the work day ends, on a weekend or when someone is looking for substance over distraction:

“For me it’s news apps. So CNN, BBC, ABC. I get up in the morning at a ridiculous hour because I am a single mum and that’s my only quiet time before it all kicks off. I look at those things.” (Australia)

Older participants sometimes noted that they seek information from more traditional sources because they feel less digitally savvy than some of the younger participants, but even these participants described a fluid relationship to getting informed, in which they check in on the news periodically throughout the day, rather than solely at scheduled times dictated by print or broadcast schedules:

“I would much rather get it like on the go and in like a snippet format, rather than to sit down and actually watch a program for like 30 minutes.” (South Africa)

Actively Creating a “Passive” News Environment

Past research has characterized these behaviors as a shift from more “active” forms of news consumption to “passive” consumption. However, many participants described, in detail, ways in which they are actively taking control of the information they consume, putting a significant level of effort into curating their feeds, seeking out new information within social platforms, and cross-referencing information across multiple sources. 

Unlike a morning television newscast, in which producers and hosts curate a selection of “top stories” for a broad audience, most people are engaging with a new kind of morning newscast for which they are the producers, curating a deluge of social media posts, app notifications, recommended stories and trending hashtags — often while the broadcast streams play in the background. 

The challenge with the new arrangement, as seen in the labors described by most participants, is which information to pay attention to. Throughout the focus groups, participants often described three driving factors in making their choices: 

• Real-life social relationships: Using friendships and other offline relationships to focus their attention on topics of interest to their communities.
• Curated social feeds: Curating the accounts and creators they follow on digital platforms, including social media, podcasts, and YouTube.
• Recommendation algorithms: Collaborating with algorithms that learn from their behavior and surface relevant information.

Real-life Social Relationships

Participants often described the role that social relationships play in anchoring their interest and attention in particular topics. Across countries and age groups, people mentioned spouses, friends, colleagues and other people in their lives, noting that these relationships will encourage them to learn more about an issue to build their own knowledge on the topic and/or as a way to invest in the relationship:

“As friends, if it’s important in their world, I’m more than willing to sort of lend an ear and an eye if it means something to them.” (Australia)

“I actively seek out certain news from time to time if I hear something from somebody on social media or text messages etc.” (United States)

“I’ll […] wait for my wife to tell me, ‘Did you hear about this or this or that?’” (Australia)

“[A]lso in our township we talk, we get information from our peers.” (South Africa)

As a few people — including one professional psychic — noted clearly, staying on top of the information that is circulating in their social communities means staying on top of both more and less trustworthy information. In other words, some information is valuable to know, even if it isn’t accurate — but as discussed below, participants also described efforts to verify relevant information:

“I usually follow a lot of psychic pages, so I usually know, like, like psychic news. Or, like, what’s happening in the psychic community, basically. So yeah, I see the good as well as the bad I guess, the scammers.” (Australia) 

“With the neighbors sometimes, you don’t believe them but you find that they are telling the truth. Sometimes you believe her, yet she has put her spiced version.” (South Africa)

Curated Feeds

Our focus groups often echoed what many studies show: social media plays a big part in how people get exposed to at least headline level news and information. One novel and striking element of the focus group discussions, however, was the way participants described actively curating the information that they receive in their newsfeeds. 

On various digital platforms, participants described in detail their engagement with a mix of hand-picked accounts that they follow:

“I have an electronic subscription to the newspaper published in the large city near me. I have intentionally followed a variety of Twitter/X accounts. Both of those sources might prompt me to seek more specific information about issues and events.” (United States)

Several participants described following professional journalists and news organizations on Facebook, X (Twitter) or Instagram:

“[W]hen I’m talking about Twitter, I think of the journalists who have a profile there and post information about their topics of choice. […] Most of those sites, the news websites, the portals — they also have profiles on media platforms.” (Brazil, from Portuguese)

“Yes, Instagram can be considered a new source, as I follow many news pages, you know, news outlets pages, they deliver news to me, so I consider Instagram to be a news provider.” (Brazil, from Portuguese)

“All the newspapers I get it on my Facebook but physical newspapers no, not really.” (South Africa)

Others described following individuals, creators and organizations who curate news and information from a particular perspective that they share, including religious, political and niche interests:

“I just genuinely follow more people that I feel are more aligned with my way of thinking. And I don’t know. I feel like that’s what other people do. They kind of subscribe to following certain yeah news, uh studios and stuff like that, that they feel like ohh, you know I can trust them and stuff like that.” (Australia)

“I think anyone can have a platform, and I think of social media when I think of a platform in the smallest sense of the word. I follow this account with 1,000 on Twitter that provide news, and good news at that, for a very niche subject.” (United States)

“I exclusively follow some people and those are the only people that I get news from.” (Australia)

As discussed in our essay on how people define news and journalism, participants sometimes saw these creators as news producers and journalists. Other participants hesitated to call the creators “journalists” even when they saw them as important sources of news:

“There is this guy. He’s always telling us facts that are happening in real time. He has this page, he’s a local person, and I trust him. And I don’t know who is behind him. I don’t know if it’s a journalist or media source. Ordinario, that guy.” (Brazil, from Portuguese)

Participants’ descriptions of their habits suggest that many people prefer to get the morning news roundup, a staple of broadcast TV and radio, through social media, email newsletters and other digital platforms that provide access to an overview of what’s happening in their world.

Recommendation Algorithms

Many participants also described platforms’ recommendation algorithms as playing an important role in shaping their news consumption, most often describing it as useful for helping them filter through the abundance of information online:

“Well, it’s good because that means I don’t need to continuously seek out what whatever content that I wanted to see. The algorithm kind of helps me. (Australia)

“X (Twitter) provides short summaries and links to national news stories as well as some ‘on the ground’ insights. I try to follow a wide variety of accounts to observe how issues might be covered differently.” (United States)

In some cases, participants described a habit of exploring trending topics, searching for hashtags and scrolling TikTok’s For You Page (FYP) as starting points for exploration. For most people “news” is seen as something relevant to more than just one’s self and family and these tools provide access to information that they perceive as having reached a level of broad cultural interest:

“I check out what’s trending on Twitter a lot, usually when there’s quite a popular topic like [named participant] said, it shows up for us, the thing at Rio Grande do Sul, the war between Israel and Palestine, and you receive information yourself.” (Brazil)

They also saw themselves as having an active role to play in ensuring that the algorithm would surface the right content that would be of interest to them:

“On Tik Tok, for instance, I usually scroll up that bar, and they deliver content related to what we stop to read or watch. Some weeks ago, about the floods in Rio Grande do Sul, I was watching a lot. Now I’m in a different vibe. I’m dieting. So, I’m getting lots of content about diets.” (Brazil, from Portuguese)

“[M]y YouTube algorithm is very much curated to the type of videos that I watch, so it’s probably more leaning to the right side of things, whereas on Reddit it’s a predominantly left-leaning app, the sort of perspective that gets fed to me is the complete opposite. So I like to find them sort of flying down the middle where it’s, you don’t fall off the deep end or either side.” (Australia)

However, even as they called them important sources of information, many also frequently described the limitations of algorithms at presenting them with a full picture of the information around them:

“They’re just showing you what you like. And I don’t think we always need to see what we like. We need to be challenged and that’s how we become educated and compassionate.” (Australia)

“I wish that there was a little bit less algorithmic stuff about news cause I definitely agree with [another respondent]. I love hearing other people’s point of views.” (Australia)

“I think their technologies are built to just show me what they think I will like, or the spin I want to read. I enjoy the technologies I use, but I don’t want just the info I might like. I just want the truth and facts, not opinions.” (United States)

As social media platforms increase their emphasis on showing users content from accounts they don’t follow, participants’ ability to control the contents of their feed and access to specific accounts will change. Additional research into the specific social media behaviors related to information access are needed to better understand the impact of these changes on the news environment.  

Putting Information in Context

As seen in several of the quotes above, participants also often noted that once they had built habits and systems for shaping which information they paid attention to, they often felt the need to verify and contextualize information that they received through their own research.

In line with past research showing an increase in individuals’ confidence in their ability to “do their own research” on important decisions in their life (alongside declining trust in institutional authority), participants described relying on their own research skills to make sense of the abundant information they encounter:

“I have a very, very well formed habit of dissecting it and reading between the lines of the information I’m getting.” (Australia)

“I’m always checking because this is how you operate online. Whenever you seek a piece of news, you have to check. You know, it’s too much online. It’s too much information that you get online.” (Brazil, from Portuguese)

“…it is really hard if you read something, everyone’s saying you got to go do your own research. You can’t just take it from that one source.” (Australia)

Across countries and individual groups, participants talked about conducting a kind of “comparison shopping” across multiple sources in order to better understand how the same story is presented by different sources. This research seems to take three main forms: verifying information across multiple sources, using search tools to build depth and adding perspectives.

Verifying Across Multiple Sources

Participants often spoke about encountering untrustworthy information online and expressed doubts about their ability to judge an individual piece of information without further context — even when it comes from traditional sources:

“Many times I can’t tell if it’s false so I’ll go to multiple different news outlets to confirm what I read or [do] a simple Google search.” (United States)

“If I see something that I question, I check multiple sources and sites, yahoo, google search and find what I’m looking for, either proving it’s legit or if it’s false. Yahoo, CNN, Fox, Reddit are some of the places I will look to for verification.” (United States)

“We are part of a community and these are people, we verify, like when you get information from a neighbor, you must ask other neighbors about it, that’s how you verify. If all of them agree with each other, then you know it’s the truth.” (South Africa)

This behavior is not limited to the more news-motivated consumers. In Brazil, our focus group took place around the time of a major transport workers strike in Rio. Several participants described first hearing about the impending strike through social media posts and friends and then checking for additional information from other sources:

“I saw that on a friend’s status. He posted that buses would go on strike on Friday. So, I actively search that information, but information is somehow just around me. I get that on Instagram, on stories. Or maybe on WhatsApp. So, this is how I consume information.” (Brazil, from Portuguese)

“You know, like, I depend on public transportation. So, I saw that a friend posted about the strike on his Instagram, I had to seek for more information, because I don’t know what I’m going to be doing on Friday.” (Brazil, from Portuguese)

“…maybe people who [don’t have other choices], who depend on going out […] you do have to seek — actively — for that information. And also, information is around me. You know, I got that information, and then I actively seeked for more about it.” (Brazil, from Portuguese)

News — the potential of a transit worker strike — was all around participants, but they turned to other sources for deeper information and awareness about the issue. 

In some cases, they want to ensure that the facts are consistent between sources:

“I would see it and I would go to the next channel or let me rather say, normally I read online. So, I would go to Opera news, read about that, then go to SABC news online and still read about the same thing, then I know it is true.” (South Africa) 

“Oh, yes, absolutely [I come across information that is misinformation or fake news]. Because maybe one media outlet reports something one way and the other something completely different.” (Brazil, from Portuguese)

In some cases, participants said they sought out individual, more trusted sources to validate new information and sources:

“So, for me when I get information from Facebook especially because in the villages we listen to the radio, I go to the radio to listen and check. If there is no announcement on the radio, then I know it’s a lie.” (South Africa)

“Yes, you know, I myself, I always check the source when I come across a piece of news or information from a source that I don’t trust, or that I’ve never seen before. I always check the source.” (Brazil, from Portuguese)

“Sometimes I only know something is false if I hear something different on another news source. To avoid false information, I try to stay with the same, most reliable news- but some false news is hard to avoid all the time.” (United States)

Using Search Tools to Build Depth

Participants discussed frequently selecting individual pieces of news or information that they encountered on social media or through personal networks and using search tools — including web search tools such as Google or Bing as well as platform-specific search tools on X, TikTok or Instagram — to learn more:

“I keep up to date through a variety of sources like Online news (New York Times, Yahoo, USA Today), Social Networking (Facebook,Twitter), Google Discover Feed. I like to know about all important events happening over the world. Generally If I find any information through the above mentioned sources and I want to learn more about it, I search on Google or Wikipedia.” (United States)

“Whenever I see something, I access X right away, I type the word there, and everything people are saying about the person and the place shows up.” (Brazil, from Portuguese)

“Some topics are suggested for you. And then you read those, and then you dive into a more thorough search.” (Brazil, from Portuguese)

One of the key reasons participants found value in search tools was their ability to cross-reference multiple sources to identify differences in the tone, framing or presentation of information. For several participants, the search engine results page — with a heterogeneous mix of outlets and headlines — gives them exactly what they want: an expansive view of a topic, often providing them with sufficient context to evaluate the original content that started their journey:

“I go to Google to check who else or you know, what other outlets are talking about that topic and what they are saying if it’s the same content, if it’s the same information that the trustworthy pages and sources are offering. I go on those pages, those outlets that I trust, that I find reliable, trustworthy, and check against the news that I read online.” (Brazil, from Portuguese)

Some participants specifically noted that clicking into individual content is less important than the ability to see differences side by side:

“There’s things and and multiple different things I see just from Google Feed News. The same story from three different outlets and it’s different on every single one…” (Australia)

Adding Perspective

Aware that they may only be seeing personalized results from the media they follow and the algorithms that curate social media feeds and prioritize search results, several participants described seeking out different perspectives on a topic in order to help them assess the bias or perspective of the original information they were researching:

“So if, for example, I follow 2 commentators on YouTube one guy’s left-leaning and the other guy’s right leaning. They’re both going to present the same news in different ways, but at least I’m aware that hey, this guy is going to be left-leaning always and that guy’s going to present his views from the point of view of a guy on the right side…” (Australia)

For some participants, seeking out additional perspectives and opinions is a critical part of how they make sense of information — but as opinion-focused content, they don’t view it as news:

“It’s like once I’ve got the kind of initial information the rest of it, it just kind of builds upon that, but I don’t really see it as news.” (Australia)

As noted previously, participants often said that they believed news is only information that is presented without opinion or bias. Adding perspectives helps them add depth to their understanding of an issue or news item, particularly when they feel that individual sources have a particular bias or political slant:

“As somebody here mentioned, you don’t trust that piece of information. And everything that is informed, you have to check. When they communicate a piece of news, that is misinformation, or is not real, they correct it, later on. You don’t know what to believe, you know about politics, like they mentioned, it was hard to tell apart what was true from what was not. The rightists were attacking everything. So, it raised questions and issues about it. So, when it’s too much, you know.” (Brazil, from Portuguese)

“Now, I would say the way that I get a “red flag” and will go do further research is when it’s emotionally charged or hyperbolic in nature. There are also news organization[s] I don’t use anymore because I just know that they will have half the truth in a way that is misleading to understanding what is going on to promote their own political slant.” (United States)

A few participants in the U.S. and Australia also referenced Ground News, a site that analyzes coverage of stories across news outlets, identifying the political lean of the outlets that are covering a story and providing links to different outlets. 

Finding the Limits of Personal Research and Personal Verification

An important driver of participants’ efforts is what they described as growing uncertainty about the accuracy of information that they encounter. Political polarization, commercial bad actors and scammers, and general misinformation were all reasons people gave for why they spend so much time digging into the snippets of information that they encounter on social media. 

Still, there was some recognition that because of the significant labor involved in this practice, people don’t always dig into information that they see in more passive ways and just accept that it’s true, even though they know there is a lot of potential for misinformation to reach them: 

“I would say that nowadays, it’s so common that we fall prey to them, and we don’t even notice. It depends on how interested you are in doing a more thorough search in seeking the source…. ​​if it’s a piece of news that has no impact on my life, it doesn’t make much of a difference. If it’s fake or not, then I don’t dive deep into that. If it’s something that has a bigger impact, then I say, I’ll do a more thorough search.” (Brazil, from Portuguese)

“I can’t always tell right away but my gut feeling might say well this doesn’t sound right. I don’t actively try to avoid false info because I don’t have the time or energy to try to weed it out. If it is there, it is there. That’s why I try to use credible sources for news, so they do that verification for me!” (United States)

Participants valued individuals, tools and organizations that reduce the labor involved in getting high-quality, accurate information. Much of the value that participants ascribed to journalists is in the skill and ability to identify and verify information and to synthesize it. They also described a range of algorithms, machine learning tools and generative AI tools that are helping them close the gap, with automated fact-checking on platforms, content summaries and content filtering.

AI summarization tools within search tools were frequently seen as performing some of the same synthesis tasks that participants were doing on their own. They were widely seen as helping save time. Participants frequently noted that they liked having access to the underlying links and references within the summary.

In South Africa, several participants noted that the Microsoft Copilot AI was especially useful for this:

“…I want variety and more information, because you find when you get information on WhatsApp, it’s not as much as when you actually go and you use Copilot to [find] the information that I’m looking for.” (South Africa)

“…with the AI, Copilot, I really like it because it’s made life so much easier because what it’s done really, it’s like taking all the data from everywhere and putting it together for you.” (South Africa)

However, there were no clear agreements among participants on how to make it easier to navigate the flood of information or about ways to limit the spread of misinformation, reflecting on the significant differences in awareness, opinions and perspectives. Our larger quantitative study, currently underway, aims to shed more light on these questions.

Conclusion

The focus group conversations demonstrated that far from a “passive” news environment, the modern digital landscape has thrust many people into an active mode of news consumption in which they are responsible for providing the news judgment, sourcing, fact-checking and synthesis skills that they frequently ascribed to journalists. 

In a way, this combination of passive and active behavior embodies how people combine news and journalism in their daily lives. In every focus group, participants articulated the value of getting top-level news from their personally curated mix of “push” sources. They also clearly expressed the importance of the points at which they take charge of the process by “pulling” from a range of different sources and mediums. The value of this deeper “pulling” activity is that it provides the rigor and thoroughness that participants also noted makes journalism most valuable.

However, what the focus groups couldn’t reveal is the extent to which participants are or are not getting the news and information they need. While they’re taking active steps to manage their information environment and news diet, their sense of self-efficacy doesn’t guarantee that they’re getting accurate or complete information. Many participants expressed doubts and a sense of unease about this as well. 

Other studies have explored different signals of news engagement (e.g., commenting, sharing, or responding to news) as measures of the active role that people play in their news environment. By these measures, news engagement and participation have declined in much of the world. But few studies have looked in depth at the patterns and paths that news consumers have adopted for navigating this new environment as indicators of news engagement and participation. 

Additional research is needed to better understand how well these new habits and methods are equipping people with the information they need to navigate the world and participate in civic life, whether through elections, community involvement or social connection. CNTI looks forward to supporting and collaborating with others in the field to further explore these questions.

More Information

This essay is the third in a series of insights drawn from these focus groups. Even as CNTI uses the full focus group discussions to inform a much larger quantitative survey in the fall, we felt it was worth sharing some insights now.

Additionally, a part of CNTI’s mission is to help synthesize research conducted across the community and the globe. To that end, it was a pleasure to see that several of the points discussed above come through in a recent report on AI in News produced by the Reuters Institute. Reinforcing findings in this emerging area of technology are especially meaningful and helpful in designing further studies. CNTI will continue to look across the research community to both synthesize and contribute to this important area of work.  

About the Defining News Initiative

The Defining News Initiative is an 18-month effort that seeks to understand how concepts of journalism, news and information access are being defined in countries around the world. In three different realms — in legislation, among the public and among journalists themselves — our research and analyses will provide clarity and insight on the importance these definitions play in safeguarding an independent news media, freedom of expression, and the public’s access to a plurality of news in ways that inform policy discussions and decision-making.

How We Conducted This Research

CNTI contracted with Langer Research Associates to recruit participants for a combination of virtual — synchronous and asynchronous — and in-person focus groups and focus groups moderators, in four target countries: Australia, Brazil, South Africa and the United States. 

These countries were selected strategically to capture geographic, cultural, and political contexts, as well as different news environments. Our recruitment efforts involved a screening questionnaire that asked potential participants about their information-seeking interest and behavior, prioritizing, but not exclusively relying on responses from individuals who reported that they keep up with events and issues of the day in some capacity. We recruited a total of 89 participants from these four countries (22 in Australia, divided into 2 groups; 25 in Brazil, divided into 2 groups; 29 in South Africa, divided into 3 groups; 15 in the U.S.), which we conducted between June 3 and June 7, 2024. In our recruitment efforts, we were intentional about maintaining diversity based on gender and age. 

Recruitment and focus group discussion materials were designed by CNTI researchers and were reviewed by Langer Research Associates, local vendors and others with research and subject matter expertise. All focus groups in Brazil were conducted virtually in Portuguese and one focus group in South Africa was conducted in-person, with participants conversing both in Zulu and English. For focus groups conducted in languages other than English, such as the ones in Brazil and South Africa, transcripts were translated into English. 

The post Focus Group Insights #3: In a Digital World, Getting the News Requires More Work, Not Less appeared first on Center for News, Technology & Innovation.

Artificial intelligence (AI) tools have been used in the news industry for over a decade to assist in automating tasks, including summarizing data from outside sources, crafting financial reports and sports stories and performing grammatical checks. However, the release of OpenAI’s ChatGPT in November 2022 ushered in an era of generative artificial intelligence that dramatically reshaped this landscape, and newsrooms’ use of AI tools has been increasing. The news industry’s adoption has been spurred by desires for efficiency, hopes of growing readership and general market pressures. These processes and decisions are becoming well documented within academia and the trade press. 

However, much less is known about the public’s awareness of and attitudes towards the use of AI in news and journalism. In June 2024, we conducted a series of focus groups with individuals across Australia, Brazil, South Africa, and the United States, to better understand how the public defines “news,” “journalism” and “journalists” and the role that each of them play in keeping individuals informed about important events and issues. We also explored how participants understand the role of technology, and AI in particular, in shaping their information access and environment.  

This essay — exploring how the public views artificial intelligence (AI) use in news and journalism — is the second in a series that highlights themes from the discussions that will inform additional research. Each focus group included participants with a mix of socio-demographics and levels of interest in keeping up with current events. More about the focus groups can be found at the end of this essay.

Focus group discussions of AI were centered on the use of generative AI in news, specifically. We wanted to know: How does the use of generative AI fit into participants’ understanding of news and journalism? How comfortable are participants with the use of AI in this context? Do they see it as improving or hurting their access to news and information? 

AI is a complex technology that is changing rapidly, and participants had a wide range of awareness of what it is, how it works, how it can be used today, and emerging applications. Participants’ knowledge — including incorrect information — played a role in shaping their opinions, both positive and negative.

Key Observations

• Participants’ thoughts about AI use in news were tied closely to the attributes they expect of journalists. As discussed in CNTI’s first focus group essay, participants expect journalists to be skilled researchers, independent thinkers and strong communicators. This plays a role in shaping their thoughts about when, how and why AI tools can be used in the context of news.
• The quality and availability of data sources feeding AI, as well as the AI models themselves, are key considerations. While not always distinguished in the conversation, there were three primary kinds of “sources” of data discussed: training data, journalists’ original data sets and information on the internet more broadly. When it came to the models themselves, there were diverging opinions about their ability to “get it right,” and those with more confidence were more comfortable with uses by journalists. 
• Meaningful transparency is critical. Participants generally favored disclosure about the use of AI technology. Many felt that disclosures should include details about why AI was used, the sources it relied on and whether or not the information had been reviewed by a human.
• This is a key moment in a still evolving area. Participants see AI technology as an important emerging technology that will dramatically impact their lives in the future; participants had positive and negative feelings about that fact, but many expressed a willingness to change their opinion about the risks and benefits of AI as the technology evolves.

We delve into these themes in detail and highlight important nuances in how the public thinks about these concepts across different contexts.

The Public’s Personal Use

Personal Use and General Attitudes About AI

Many participants drew on their own experiences using AI to ground their perspectives about the use of the technology in news. From awareness and use to general attitudes about technology, participants’ context for the discussion varied dramatically across and within groups. Several participants said that they had not used AI tools; some said that they did not feel well enough informed about the topic to weigh in on the discussion.

Of those who did have experience with AI, many were familiar with grammar tools that implement AI to improve language and word choice; several participants had also used tools that allow them to generate text, and described using AI to draft emails, letters and other documents. 

“…But I myself as a student, I have so much to do, I need some help, just a foundation of something that I am going to write, something to add to it.” (Brazil, from Portuguese)

“I’ve used it to write a couple of things that I just honestly didn’t have the time to really sit and think.” (Australia)

Some participants described using AI tools to help them do research and learn new information. In some cases, people described using AI assistants embedded in search platforms and mobile devices, as well as standalone apps like ChatGPT. Some described using these tools to replace traditional search queries, with the resulting output answering a specific question; others focused on their use of AI to get started on exploring a topic or an idea in a new way. 

“If you don’t…quite know how to learn something, you can just chuck a prompt in there.” (Australia)

“If you’re using Edge […] you will then have a chat box which is an AI, right, where you can then communicate as if you are communicating with a human. That way it helps you to narrow down what you’re asking, and it actually searches on your behalf for exactly what you are looking for.” (South Africa)

Few participants said they had used software to generate images, and many expressed negative feelings about AI-generated or -edited images that they had encountered.

“We’ve seen or heard of images being shared online that were completely made from AI, and it’s amazing how many people don’t realize that it’s fake.” (United States)

Participants across multiple groups and countries raised concerns about the impact of increasing AI usage (in general) on critical thinking skills, worrying that use of these tools will encourage intellectual or creative laziness among users. 

“It makes us to not think actively, not think aggressively, but also not become creative in how we think. We then rely on machines to do the work.” (South Africa)

These concerns were almost exclusively directed at other people’s use of AI technologies — and they were often raised in the context where learning is a key task for the end user: students, trainees, early career professionals, and journalists were all named as examples.

“As an educator, I don’t want my students using AI to write their essays. It is important for students to understand the writing process and be able to express their thoughts through writing.” (United States)

“I’ve used ChatGPT just to ask a general question and I’ve found that the answer generally is very general … The thing is, if you use that in that context, you’re not actually learning because someone’s giving you the answer, whereas you’ve actually learned … analytical skills or research skills or how to analyse the topic, you’ll actually come out better in the end.” (Australia)

Use of AI To Get Informed

Many participants saw clear advantages to using AI tools to get informed on important topics and issues, using AI-assisted search tools or conversational tools like ChatGPT. Many participants described having AI summarize available and relevant information on a topic. They described it as making it “easier” and “faster” to learn new information. 

“AI will give me 10x more than what humans can give me.” (South Africa)

In some cases, participants described such AI tools as a good way of getting informed on current events and issues; AI assistants embedded in search tools, in particular, played a role in providing news and information to them. 

“So if you use the engines, using AI, it makes your search reliable and important. So it’s exactly what you’re getting from the radio — what you can get there and even get data and more of the in-depth information.” (South Africa)

“With the AI, co-pilot, I really like it because it’s made life so much easier because what it’s done really, it’s like taking all the data from everywhere and putting it together for you […]. And from that, the AI is generating what we as humans have inputted and then it distributes to everyone, everywhere.” (South Africa)

The majority of participants did not have an expectation of full accuracy when using AI tools in this way. In using AI tools to inform themselves, participants often noted that they believed additional verification steps are needed to confirm that the information they receive from generative AI tools is correct.

“[The] AI is … getting different types of information throughout, sourcing that information for me, and is giving me that information. Then what I need to do, I need to then cross-check it…” (South Africa) 

Some participants referenced following links to sources provided by AI assistants or described evaluating the accuracy of AI-generated information based on their own knowledge. Several participants said that this is similar to the way they check information and news that they get from other non-AI sources, including radio, mobile phones and social media platforms.

Use By Journalists

A Nuanced View of How Generative AI Can Be Used by Journalists

Although many participants have found generative AI to be helpful for informing themselves, they expressed significantly more discomfort with journalists and news organizations using the tools to create published information. 

“I am skeptical of the ‘news’ generated by AI. Not opposed to it. Just approaching it with a little skepticism.” (United States)

“When you’re creating something for your personal use, it’s okay. But for professionals, journalists, it’s not acceptable.” (Brazil, from Portuguese)

However, participants distinguished between many kinds of uses, and those that had the most frequent support among participants were those most similar to ones participants reported using in their own lives, suggesting that familiarity with and experience with AI technology helps people feel more comfortable with its use by others, an idea supported by existing research.

Some of the discomfort seemed tied to participants’ expectations that journalists be highly skilled researchers and thus do their own research. This informed some participants’ negative attitudes about journalists’ use of AI tools:

“In my opinion, if they are using AI, then what is the need for the reporters in the first place?” (United States)

“Someone who went to school for years, the journalist, someone who went to school for years now, they cannot think for themselves. They use computer software to think for them. I don’t think that’s a good thing.” (South Africa)

“No, we’re talking about people who perform work in journalism. They don’t have to use AI software, because that person already has the content, the person has to write about the content, they are not creating that content, because the creation of content is considered fake news.” (Brazil, from Portuguese)

However, participants often cited those same skills as reasons journalists could use AI, as long as they ensured that the information generated was accurate. 

“I think it’s convenient, it’s easy, but it has to be true. And you have to revise it, and you have to evaluate it. So, you don’t run the risk of being just a copy.”  (Brazil, from Portuguese)

“I am fine with people creating content like that to save time but they need to double check because AI can’t always be reliable. Sharing is fine as well but the person who shares should verify the credibility before sharing. That goes for reporters and people working in news as well.”  (United States)

Some participants said that their perceptions of their local news organizations would shift to becoming less trusting of the content these organizations produced if AI was used to create it without that layer of oversight. 

“If I knew that my local news organization was using AI to get their news and write their news stories, it would make me very distrusting of them.” (United States)

“I think that it would somehow lose some credibility because somehow, you’re going to believe that it’s not 100% trustworthy, that you’re going to come across some misinformation there.” (Brazil, from Portuguese)

Two key factors impacted participants’ general sense of when and how generative AI could be used in news, including their perception of the quality of the data available to AI, and the ability of AI models to accurately synthesize information given specific expectations for news. In the following sections, we explore these concerns.

The Quality of the Data in AI Tools Matters Greatly …

News is about facts for most people, and whether or not AI is used, participants cared deeply about the sources of information used to produce the information that they rely on. They frequently raised questions, voiced concerns, or expressed excitement about “sources” as they weighed both their own use of AI tools and use by journalists and news organizations.

“What I’d like to know is, […] Is it generic or is it from a source?” (South Africa)

“I would like to know the sources from which the data was extracted and summarized by the AI.” (United States)

“Depends on where their sources come from, I suppose.” (Australia)

Many participants seemed to understand that AI tools use vast amounts of data to power their models and were broadly aware that the quality and availability of data sources and the models used by AI have an impact on the quality of the outputs. 

While participants did not always explicitly distinguish between them in conversation, there were three primary ways in which they understood data sources as playing a role in the quality of generative AI outputs. 

Training data: Throughout the discussion, participants talking about AI’s “sources” sometimes seemed to be referring to the data used to train AI models, as in discussions of conversations with ChatGPT. As discussed above, many participants felt that this information was insufficiently reliable on its own and requires some degree of verification.

“It can be wrong because AI only uses the data it’s learned or has been fed.” (Australia)

“And so I wouldn’t rely on the journalism that comes from it [AI] because it’s information that is sourced from somewhere else and taken and put into one app.” (South Africa) 

Information on the Internet more broadly: In other cases, participants discussed the use of AI-powered search tools that help them find and summarize information from the Internet. In this case, “sources” often seemed to refer to online content accessed by the search engine. Often, their perception of the quality of information on the Internet affected their sense of the reliability of these tools. 

“When you go to the AI and you ask for something, it gives you all of that information at once, where you don’t have to go and search on this specific site or that specific site that Google is giving you.” (South Africa)

“I have peace of mind because they [AI tools] are basing themselves on information that is online. So, they are real information.” (Brazil, from Portuguese)

“I really just don’t have a good feeling about AI. I haven’t fleshed out all my thoughts on it, but I just know that the Internet has always led people astray when it comes to current events, and I have no idea who is creating the content behind AI, but I don’t trust it.” (United States)

Journalists’ own original data sets: When discussing the use of AI by reporters, participants frequently discussed whether reporters would be providing information to the AI for synthesis and summary. (And indeed, some news organizations are building such models.) This differed from their own use of AI, and was the most common use case in which participants said they would be comfortable with reporters using AI tools: 

“[I]s it okay if you create news content using AI? My answer is, if the AI uses content that is true facts, [if] the person who is asking for that creation feeds that tool with factual information.” (Brazil, from Portuguese)

“I don’t know how artificial intelligence could help in creating a piece of news […] but the facts themselves have to be found and tracked by the journalist.”  (Brazil, from Portuguese)

South African participants in one group also raised the possibility that emerging technology such as drones could provide unique, useful data to AI tools that would make reporting faster and safer for journalists: 

“With the technology that has been built, it allows them to actually give us the coverage all around and live and happening right now. So I think that’s the exciting thing. […] we still will get that excitement of having someone who’s at the scene by using drones, right?” (South Africa)

“With this new technology, you don’t have to be at war. You can use drones. So me personally, I think AI is the better.” (South Africa)

… As Does the Quality of the Models Themselves 

Most participants were uncomfortable with journalists using AI to generate the entire content of a published story, but they had diverging opinions about how reporters could use AI tools for summarization and synthesis. Perceptions about the ability of AI models to accurately synthesize information from the underlying data informed these opinions, as well as the data quality concerns described above. 

Participants who believed that the AI tools used by journalists would have access to abundant, accurate, and up-to-date information were the most likely to see the value of AI for people creating journalism content. 

“As long as reporters are getting intel from MULTIPLE, trustworthy sources, I think the use of AI in news organizations is okay.” (United States) 

“The computer is able to make suggestions because you’re drawing from a huge pool of information. [A journalist] may have once been able to read one book, but now she’s got a pool of information of the latest research, the latest this, the latest that.” (Australia)

“I would only want AI used when there is a critical mass of information available as opposed to limited or scattered information.” (United States)

In some cases, participants specifically said they would not want journalists and news organizations to use familiar, off-the-shelf AI tools such as ChatGPT, to generate content, referencing their concerns about the quality of the output of such tools. 

“…people who use ChatGPT in helping you formatting a piece of news, it’s okay. But the issue is if you use a text that has already been created as a piece of information. That is true and ChatGPT is not 100% reliable.” (Brazil, from Portuguese)

In some areas of coverage, participants expressed concerns that there may not be sufficient information for AI to leverage in content creation, either because of gaps in the sources AI is trained on — or because of gaps in what is known more broadly by the public. 

“These things are trained to generate information that’s already there. But not information that we don’t know.” (South Africa)

Some participants saw the opportunity for AI tools to help reporters create content if they provided original research and facts for the software to work with. 

“…the software can help the person [journalist] but it’s not acceptable to write from scratch.” (Brazil, from Portuguese)

Participants had specific concerns about AI models’ ability to successfully generate information with characteristics that are particularly important in a news context. 

Recency: Multiple participants specifically raised concerns that AI tools would lack access to sufficiently recent information:

“News is often cutting-edge and I am concerned about how much AI had the opportunity to learn about breaking news.” (United States)

“[If] you ask it [AI chatbot] about information that has happened probably this year, you might find that it won’t have full information about it. But if you ask it about something that happened five or six years back, then it can give you the entire information. So for me, there is some danger there.” (South Africa)

Local relevance: Other participants voiced concerns that AI-generated news content would be unable to provide a local perspective for current and recent events. 

“So I don’t think the artificial intelligence has those spices, those local spices where you make news to be more interesting. So we’re going to lose a lot when it comes to journalism.” (South Africa)

Nuance: Some participants expressed concerns about AI-generated content “losing” important context and detail when working with large volumes of data, wondering how divergent information or lesser-known facts are incorporated into summarized data. 

“It gathers, but does not credit the original content creators — and may even combine content that was never written to be associated. AI removes ‘nuance’ — subtle expressions and meanings that are important.” (United States)

Neutrality or bias? Some participants said they believe that AI-generated content could be more neutral than that generated by humans:

“If you really want something to be unbiased, then computer-generated is the way to go because computers cannot be biased, partisan, racist, cranky, or anything else. In finance, a lot of bias has been removed by using AI algorithms for things like underwriting because computers cannot be biased the way people can be, they just compute.” (United States)

However, others raised concerns about the potential for bias in AI-generated content, citing bias in models, or raising concerns about the potential bias of the sources available to a particular AI tool.

“I don’t trust many things AI generated because who is generating the information that the AI program is using? What’s their biases? What angle is the information coming from? I’d be more comfortable knowing who and what company the engineer behind the AI program works for.” (United States)

These are widely discussed issues in technology and media circles, but most participants lacked familiarity with the debate. 

An Important Role for Journalists in AI Oversight

“I love what [AI] is doing to us, because it’s bringing technology and humans together. But we need to also manage that as well.” (South Africa)

Because of their concerns about data quality and the limitations of AI models, participants frequently described journalists as playing an important oversight role in a landscape that includes more AI-produced content. 

Journalists are expected to have strong research skills, and many participants saw a need for them to check the accuracy of information provided by AI, including fact-checking information, providing nuance, as well as supplying previously unknown information, which would not be available to AI tools. 

In some cases, they described the potential for journalists to draft television scripts or news articles using AI tools — but often with the caveat that additional research or fact-checking would be required.  

“…it is ok for anchors and organizations to have quick summaries but [they] still [need to be] diligent to do second-level research.” (United States)

“To make me more comfortable with computer-generated content, I would like to know whether it was thoroughly verified and fact-checked by a human.” (United States)

“There are systems that you need to follow. And also just to verify that this information is true and it will give you details in terms of the background of that source, whether it’s fake or is it verified information or not.” (South Africa)

Other participants noted that they would want journalists to review content for a more “human perspective” — to ensure that it doesn’t miss important framing or nuance.

“…it should be used as reference for help but still need human involvement especially on sensitive topic.” (United States)

“It’s great for reporting facts, but it might not be able to give that kind of analysis maybe or human input…” (Australia)

Some participants saw the opportunity for AI tools to help reporters create content if they provided original research and facts for the software to work with. 

“AI might be able to provide a summary of the “ingredients” to this story, but to simply copy/paste what AI generates would be irresponsible and would likely miss important perspectives and information.” (United States)

“[The AI] cannot go alone to collect and present the information. They are programmed. They depend on the journalist. The journalist, they collect the information, they feed the AI, so meaning the AI, they cannot work alone. They need the journalist in order to function…” (South Africa)

Several participants also noted that while AI could be used for summarizing general fact-based information, it was “unacceptable” for it to generate opinion-based content. This aligned with participants’ definitions for news, in general, which should not include opinion. 

“So, if it’s an opinion-based piece of news or an article or something, then it has to be written by the person. It has to be written by whoever is giving that opinion.” (Brazil, from Portuguese)

In other portions of our focus group discussion, many participants expressed a belief that journalists should be mission driven and have a strong moral or ethical approach to their work.  This was mirrored in a common theme among participants that using generative AI methods was only acceptable if the person using them did so in a responsible manner. Participants were most comfortable with AI usage by trusted individuals who would use it “responsibly.”  

“…if it’s a person I trust, if that person uses it responsibly, it’s okay.” (Brazil, from Portuguese)

As a result, several individuals contended that AI was unlikely to be able to successfully provide this detail on topics such as politics and health, where nuance or rapidly changing information is required. 

“Unless it was a very cut and dry topic, with only one right “answer”, I wouldn’t trust AI, because let’s be real — what topics are so black and white? Not many.” (United States)

Technical and Aesthetic Improvements Have Broad (But Not Universal) Support

“I think if it’s the manual labor part of things — if you’re putting a video together as a news organization … and it’s the frills and it’s the bows, it’s the presentation…. I think that’s good.” (Australia) 

Journalists are expected to communicate information to the public, and many participants saw potential advantages for reporters using the technology to improve the aesthetic or technical qualities of the work by assisting with drafting, copyedits and graphic design applications. 

As described above, participants are very familiar with the use of AI tools that help them improve their own writing mechanics. Most believed that generative AI technologies are appropriate for tasks such as grammatical checks and drafting emails, which they view as having clear rules that software could follow, with minimal oversight. 

“I don’t really have an issue with, you know, spell check or Grammarly, the little app that you can have to fix your grammar. I think that makes sense.” (Australia)

Many participants also said they are generally comfortable with journalists using generative AI tools to help “find better words” or make content “more professional,” in the words of a few (South African) participants.

“If you have done your own research, if you’ve written up your whole thing, you know and you’re just using it to edit it, polish it up, stuff like that, you’ve done like the groundwork yourself. I feel like I’m more, I’m more comfortable with that.” (Australia)

Other participants disagreed with the use of AI to edit or “polish” content, arguing that using AI would cause content to lose individuality. 

“For me, when I say it’s lazy, it’s because it doesn’t give you the component of writing your own words to deliver your own version and your own style.” (South Africa)

Visual Content Creation by Generative AI Strongly Opposed

Notably, no participants said that they supported news organizations and journalists using generative AI to create or enhance images and videos in their coverage, a finding supported by recent research from Reuters.

“I definitely like seeing what’s actually happening. I don’t want some AI generated computer-generated stuff in front of me.” (Australia) 

In the case of AI, people frequently described synthetic, AI-generated images as fake; many people have encountered faked images on social media, increasing their level of distrust in AI-generated imagery. 

“It’s not real and shouldn’t be made.” (United States)

In the Australian groups, participants also discussed negative examples of news organizations that had used AI to alter real images (for example, expanding the length of a photo to include details that were not in the original and altering a female lawmaker’s dress to make it more revealing).  One participant also mentioned the use of an AI-generated host on a news program from Chinese state-run media: 

“It’ll be reporting on, like, accurate news, where they’ve an actual figure with the voice generated by AI. So everything is AI — but you can’t tell the difference.” (Australia)

In discussions with participants about their preferences for news content, we previously noted that participants named photos and videos as important elements of “on the scene” reporting that gave them confidence in the quality of news content. Likely because images are perceived as critical to understanding news events, synthetic or AI-altered images were widely seen as not acceptable in news content.

“I don’t think news sources should be using AI to create videos, because I prefer if they gave me the real video or, like, the real pictures of what happened.” (Australia)

Helpful Forms of AI Disclosure

“It is okay to use AI as long as it’s stated beforehand. This would give people the ability to make their own choice whether to trust the information or not.” (United States)

The vast majority of participants said that they would feel most comfortable with news organizations using AI if they were informed about its use. Encouragingly, the reasons that people gave for wanting to be informed, and the types of information they would want in such a disclosure, were nuanced. 

• An opportunity to learn: Many individuals said that labeling AI-generated content would give them the opportunity to evaluate the quality of the information themselves. Because they have uncertainty about the use of AI in news, participants often indicated that it would help them exercise their own judgment in an emerging context. 

“I think labeling it would be good. It wouldn’t immediately turn me off something. I might be interested to read and see if it’s the same quality…” (Australia)

“…I think that people don’t tell us that they are used to this computer-generated content. […] And I have to know the source, because then I know if I can trust it or not.” (Brazil, from Portuguese)

• Giving credit where due: Participants in several groups — notably Australia and the US — said that disclosure was required in order to provide a full picture of the various sources and creators of the content. Not disclosing the use of AI would be “taking credit” for work that was done by another entity, or akin to plagiarism.

“…I believe that if the image was created by artificial intelligence, as a consequence of copyrights, it has to be mentioned that it was created by artificial intelligence.”  (Brazil, from Portuguese)

“If you haven’t developed it yourself, you can’t put your name to it. You need to disclose this as the source.” (Australia) 

• Organizational trust matters. For a few participants, disclosure is an important part of maintaining trust in the organization’s overall transparency and standards. 

“Accountability. You need to be able to penalize the company, instead of just blaming it on the algorithm, because the algorithm cannot take responsibility.” (Australia)

“Just be open and honest. If you’re not gonna be honest, then you’re deceiving people and I don’t like that.” (Australia) 

For many participants, this means more than disclosing when AI is used — they were also interested in how AI was used and what benefits or improvements were gained from doing so.

“I just would want to know why you would need to use AI-generated information on a report. Like, what part are you using? Why would you want to do it?” (Australia)

Not all uses need to be disclosed. A few participants who found it acceptable to use AI only for limited technical purposes — spell check, graphic design, and related “polish” activities — said that they did not see a need for disclosure.

“It’s not necessary to inform me that they used artificial intelligence in writing.” (Brazil, from Portuguese)

Conclusion

Broadly, participants seem to believe that we are at a moment of transition, and that their experience with generative AI tools will change. 

“The benefit is more access to information. One thing we must understand about AI is it is still in the beginning stages, so there is obviously going to be wrong things, but we are fixing it as we go.  Facebook is not the same that you found in 2007, it is now advanced, so you can’t judge AI now.“ (South Africa)

“It has the potential to be dangerous and […] human beings are gullible and take the easy way out. So. I think it’s opened a very dangerous door just the same as the Internet opened a very dangerous door and look, damage it’s done to our kids.” (Australia)

Participants often described the expansion of AI technology as unavoidable, despite their concerns about the implications of its broad use and adoption. They drew parallels to a variety of automation technologies and the spread of the Internet, citing concerns about the ways in which technology can transform society, sometimes for the worse. 

However, they also see ways in which AI can still be improved to avoid negative outcomes. The discussions demonstrated a desire for AI developers to ensure that models and data sources improve accuracy, for journalists to ensure the accuracy and nuance of information in their environment, and for news organizations to use AI in responsible, additive ways. As the technology continues to evolve, public expectations and perceptions will likely evolve as well. 

More Information

This essay is the second in a series of insights drawn from these focus groups. Even as CNTI uses the full focus group discussions to inform a much larger quantitative survey in the fall, we felt it was worth sharing some insights now. Forthcoming insight pieces will explore participants’ thoughts on uses of AI in journalism, the role of technology in getting informed, and decision-making around who to rely on and how to verify information.

Additionally, a part of CNTI’s mission is to help synthesize research conducted across the community and the globe. To that end, it was a pleasure to see that several of the points discussed above come through in a recent report on AI in News produced by the Reuters Institute. Reinforcing findings in this emerging area of technology are especially meaningful and helpful in designing further studies. CNTI will continue to look across the research community to both synthesize and contribute to this important area of work.  

About the Defining News Initiative

The Defining News Initiative is an 18-month effort that seeks to understand how concepts of journalism, news and information access are being defined in countries around the world. In three different realms — in legislation, among the public and among journalists themselves — our research and analyses will provide clarity and insight on the importance these definitions play in safeguarding an independent news media, freedom of expression, and the public’s access to a plurality of news in ways that inform policy discussions and decision-making.

How We Conducted This Research

CNTI contracted with Langer Research Associates to recruit participants for a combination of virtual — synchronous and asynchronous — and in-person focus groups and focus groups moderators, in four target countries: Australia, Brazil, South Africa and the United States. 

These countries were selected strategically to capture geographic, cultural, and political contexts, as well as different news environments. Our recruitment efforts involved a screening questionnaire that asked potential participants about their information-seeking interest and behavior, prioritizing, but not exclusively relying on responses from individuals who reported that they keep up with events and issues of the day in some capacity. We recruited a total of 89 participants from these four countries (22 in Australia, divided into 2 groups; 25 in Brazil, divided into 2 groups; 29 in South Africa, divided into 3 groups; 15 in the U.S.), which we conducted between June 3 and June 7, 2024. In our recruitment efforts, we were intentional about maintaining diversity based on gender and age. 

Recruitment and focus group discussion materials were designed by CNTI researchers and were reviewed by Langer Research Associates, local vendors and others with research and subject matter expertise. All focus groups in Brazil were conducted virtually in Portuguese and one focus group in South Africa was conducted in-person, with participants conversing both in Zulu and English. For focus groups conducted in languages other than English, such as the ones in Brazil and South Africa, transcripts were translated into English. 

The post Focus Group Insights #2: Perceptions of Artificial Intelligence Use in News and Journalism appeared first on Center for News, Technology & Innovation.

Among professional circles, there is a great deal of concern about the future of journalism. Much of the concern stems from recent developments that directly affect the ability of news organizations to operate: trimmed budgets, laid off staff, and in many cases, shuttered doors around the world. There is a persistent sense among both media and democracy professionals that, despite the emergence of new digital outlets and an abundance of content across many more digital platforms, the supply of high-quality journalism has taken a hit, and with it the availability of critical information to inform civic participation. At the same time, surveys of news consumers report a sizable number of individuals are now actively avoiding or managing their exposure to news at least some of the time or around particular topics. 

As society aims to address some of the challenges that may contribute to these twin problems of supply and demand, CNTI is examining how legislation and other actions can best support a truly sustainable news ecosystem, including what news and journalism mean to people in a shifting media landscape. 

In June 2024, we conducted a series of focus groups with individuals across Australia, Brazil, South Africa and the United States to better understand how the public defines “news,” “journalism” and “journalists” and the role that each of them play in keeping individuals informed about important events and issues. We also explored how participants understand the role of technology, and AI in particular, in shaping their information access and environment. This essay — exploring how the participants define the key terms of “news,” “journalism” and “journalist” — is the first in a series that highlights themes from the discussions. We felt that there were clear enough themes to share publicly with the note that these are qualitative insights that will be further explored in quantitative surveys in each of the four countries.

Each focus group included participants with a mix of socio-demographics and levels of interest in keeping up with current events. More information about the focus groups can be found at the end of this essay. 

Key Observations

• News is described as that which is recent, factual and relevant to a broad audience (not just an individual or small group). Participants understand news to be something that is verifiable but also that it can range widely in terms of quality, from reliable, verified information to biased or inaccurate “fake news.”
• Journalism is a process of independent, thorough research and storytelling, informed by a clear set of ethical and moral values; news produced through this process — sometimes also referred to as journalism — was seen positively. Understood this way, most participants agreed that journalism can be and is done by non-journalists when certain other criteria are met, such as the ability to communicate information effectively to a broad audience.
• Journalist is a term that is used in multiple, sometimes conflicting, ways. Participants used “journalist” to refer both to individuals who follow the process of journalism to create news, and to individuals creating content within recognizable media organizations, regardless of their work quality. Not all journalists are viewed positively, and one does not have to be a journalist to produce journalism.

We delve into these themes in detail and highlight important nuances in how the public thinks about these concepts across different contexts.

What makes news, news?

There were a few key criteria that distinguished “news” from other kinds of information among participants across the four countries: recency, relevance, and verifiability were key ideas that came up for many people, with some broad variations in how those terms were defined.

Recency

Across the majority of groups, participants said that news needs to be about recent events or new information. This was particularly salient for events with dynamically changing conditions, from weather and traffic to natural disasters and elections.

“The question is in the answer. When it’s news, when it’s something we don’t know about, it’s new to us.” (Brazil, from Portuguese)

Impact

In order to be news, and not just information, many participants expressed a sense that it was new information of importance that reaches a broad audience — not just themselves.

“What makes information news is something that is current, something that is affecting our youth, something that is affecting the citizens of South Africa.” (South Africa)

“News is something that reaches many people.” (Brazil, from Portuguese)

Verifiability

Participants agreed that news is about accurate facts — and accurate facts can be verified. Many participants characterized news as having qualities that make it less subject to interpretation and more possible to confirm its accuracy:

Simple: The examples that participants gave of “news” were often very simple — someone did something, an event happened, someone died, a team won.

“When I think of news, I just want an informational update of highlights of impactful events. I do not want an angle, an opinion, an emotion, or something filtered. If XYZ event happened, just tell me that.” (United States)

“Yes. I would say that news has to be unbiased. If it’s biased, it’s an opinion, not news.” (Brazil, from Portuguese)

Direct: A number of participants expressed a preference for news to be information provided by a source with first-hand knowledge or experience. Original social content — videos and photos, in particular — and on-the-scene reporting helped these participants feel that the news was more “reliable.”  

“The news that is given to you live is true. But if it’s published later on, or if it’s communicated later on, then it can be edited and not necessarily true.” (Brazil, from Portuguese)

“So normally, we trust the journalist that is reporting on the news cause you see them on the TV, they are actually doing the interview direct. […] So there’s no third party involved here. I don’t like third party’s opinions. I like to see the real thing. So if the journalist is having an interview with anybody, a minister or whoever, you get it straight from the horse’s mouth, as you would say it.” (South Africa)

Across groups and countries, “news” existed on a spectrum of quality. 

Participants reported encountering an abundance of news that is recent and high-impact but that they felt required additional verification. (We will return to this topic in a future essay.)

Several participants used the term “fake news” to describe such information — typically, a story about recent events with broad reach or impact. Among participants who valued neutrality or directness of the reporting, factually correct news that lacked these qualities was nearly as likely to be deemed “fake news” as factually inaccurate information.

“There’s more fake news on the Internet. There’s more biased information on portals and news providers.” (Brazil, from Portuguese)

“Politics is what you get the most, the biggest amount of fake news. Because when you’re talking about education and health, it always has that political bias. At the end of the day, there’s lots of fake news.” (Brazil, from Portuguese)

At the other end of the spectrum, frequently, was journalism. 

What makes journalism, journalism?

Across countries and specific focus groups, participants had a strongly consistent understanding of journalism. The majority of participants understood journalism to be a kind of information that is defined by the way in which it was produced: 

A Robust and Ethical Process of Research and Reporting

“It’s the process to get the facts straight and correct before presenting to the public as news. Journalism, when done correctly, is the work done before it gets to the news.” (United States)

“Journalism is how you seek that news, and how you present it to the public. The journalistic work happens before the news is communicated and gets to you.” (Brazil, from Portuguese)

“For me, journalism is more like a systematic way of collecting and then distributing. News can be like, the new store opens down there — and journalism will be, like, people literally coming in, taking images, asking the shop owner about it.” (Australia)

Because participants understood journalism as activities done by a specific actor, they often described those activities as a way of explaining what makes journalism distinct from news and other information. (Many used the word “journalist” to describe the actor; we’ll look closer at their understanding of this term in the next section.) Typically, journalism is understood to be:

Well-Researched

It was frequently important to participants that someone doing journalism should have specific skills that allow them to uncover details of information that other people would not have the ability or time to discover.

“What makes a journalist basically is somebody who has the knowledge, who has the skills, and has the certain type of skill of double checking before portraying the story.” (South Africa)

Mission-Driven and Ethical

Participants broadly understood journalism to be mission-driven, with practitioners motivated by ethical and moral qualities, in pursuit of the truth. 

“They need to be ethical. They need to be unbiased, and they need to be able to put their feelings aside, should the topic be something that is what we call a conflict of interest to them.” (South Africa)

“A journalist has to be passionate about what he does. It has to be a calling. […] Because when the person knows that their work will have an impact on people, will change people’ lives, they’re passionate about it.” (Brazil, from Portuguese)

“If it was about their nature, you would want someone who was ethical […] inquisitive.” (Australia) 

Well-Crafted

As a form of news, journalism needs to reach an audience to be considered journalism. Participants nearly always referenced communicating as a part of the process of journalism, alongside research.

“That information could be kept digitally, or it could be kept in a book, or it could be kept in a podcast so that other people can listen through, or it could be kept visually for people who can’t read or something, but they can see, they can tell details. That’s the journey of journalism.” (South Africa)

“News is happening, journalism is storytelling.” (Australia)

“I think that’s where, then, the need for journalism comes in: to take raw information and be able to push it in a way that certain people can understand it to their best level of understanding.” (South Africa)

Who’s a journalist and who can do journalism?

We encountered diverging opinions about what makes a journalist, a journalist and who can produce journalism, with disparate viewpoints expressed by participants within the focus groups, as well as differences by country.  

In the beginning of most conversations about what “journalism” is, many participants used the word “journalist” to describe the individuals doing the work of journalism and producing high-quality news that they could rely on. 

Throughout the conversations, they also regularly used the word “journalist” to refer to professionals working inside mainstream news organizations. In many cases, specific professionals working inside mainstream news organizations — most often television and digital outlets — were raised as examples of individuals whose work lacked sufficient research depth, ethical standards, or social value to be considered journalism. 

As the conversation deepened, we saw participants wrestle with the tension between these two uses. In conversations, we noted that a decline of trust in institutions and formal expertise, growing use of technology, and even the changing nature of work seem to be driving a shift in how individuals judge someone’s qualifications to do journalism. 

Ultimately, most participants agreed that journalism, as a rigorous, ethical process, could be done by individuals who are not professional journalists, as long as certain criteria were met — and often done in ways that are superior to journalists. It was even suggested by some that artificial intelligence could be a journalist if it met those criteria. (We will explore this topic in a future essay.) 

There were three primary characteristics that participants identified as necessary for someone doing journalism. 

High-Level Skills

Participants expect journalism to include in-depth research, meet ethical standards, and be communicated clearly. As a result, they understand the individuals creating it to require a significant set of skills.

“A journalist is someone who would seek the truth and present it to us accurately in a fair and unbiased manner, and someone who can take accountability for their actions…And someone who is transparent about their sources, as well as someone who can fact check their own information, and someone who is rigorous and really determined to uncover the truth.” (South Africa)

“I know that they have, like, a code of ethics that they kind of need to follow, and I think that’s important. […] Obviously, the rules that apply to all people in a profession, you know, like builders have rules, journalists have rules… if people are taking that on, then anyone can be a journalist.” (Australia)

A number of participants, particularly in Brazil, said that the skills required to do journalism needed to be acquired through formal training or education. 

“I believe that they have to go to school and be educated about the topic because they have to learn ethics, they have to learn lots of different topics. You have to know it all to be considered a journalist.” (Brazil, from Portuguese)

“[T]hough in order for you to be a real one [journalist], then that’s where you need to go and study media or study communication, then you become a real journalist.” (South Africa)

“To me, Journalism is a profession that required years of training and experience. Anyone can report on something they saw or experienced, but a Journalist has training on how specifically how to best provide that communication in unbiased, succinct, accurate ways.” (United States)

Other participants put less emphasis on formal training, and more on personal aptitude, on-the-job learning or on transferable skills from other analytical roles; often, these participants referenced this expectation as new, based on changing market conditions or cultural expectations:

“I think some people just have a natural talent for it where, you know, they don’t actually need to go and study or get a qualification. And you find that they go ahead and make it on their own.” (South Africa)

“I was saying that nowadays, you don’t need a diploma anymore. But a person who gives a piece of news goes after facts. The person comes across facts, works, that fact that person is ethical. The person goes after the information that substantiates that news. That person can be considered a journalist, but many have a diploma as well.” (Brazil, from Portuguese)

In neither case did the participant consider it necessary or even advantageous for someone producing journalism to work within a formal institution or organization.

Iconoclasm

Participants described journalism creators as independent from political or financial influences, unbiased, and willing to pursue unpopular stories. In several instances, in-depth research was valued because it can yield new information that challenges the status quo or official narratives.

“Journalists can dig up information and talk to people that police may not be able to and then it is their job or responsibility to report all of the information, not just the parts that fit the narrative that the majority may want.” (United States)

“I would say it’s presenting the unpopular side of things. You think of, say, someone like Louis Theroux. He does documentaries on people who are not pleasant people, but he shows them in a light where they are human, where you may come to understand why or how they’ve come to form their beliefs or do what they’ve done. […] It’s actually showing them for the multifaceted individual that they are and goes that deeper level.” (Australia)

While some participants believed that professional journalists are the best practitioners of journalism today, many more expressed concerns about mainstream media organizations and television broadcast personalities for lacking this quality. 

Participants cited political bias, personal ambition, corporate consolidation, and both individual and corporate financial incentives as reasons professional journalists veer from producing journalism. This follows years of research showing a global decline in trust of institutions, including media organizations. These concerns were particularly prevalent in South Africa and the U.S., but were present in all countries. 

“I mean they should be just covering the news to cover the news but honestly, they hype stuff up and have their own aims in their careers. They are writers/communicators and benefit from making a name and a reputation for themselves. I’ve seen so much stuff that is just over-hyped but it creates viewership.“ (United States)

“Journalists in South Africa are not independent; they are to push an agenda. […] They are told, ‘you can’t write about that.'” (South Africa, from Zulu/English)

This also sometimes meant that individual content creators — especially YouTubers and podcasters — with more extreme/less mainstream political or social views were cited as examples of non-journalists doing journalism. 

“[W]hen you go to and watch independent journalists, especially on YouTube, that have no funding, no backing except for the [internal] moral belief of what they’re reporting on, you can take it a lot more earnestly rather than having it be like a monetary agenda behind it.” (Australia)

A Platform

For many participants, someone doing journalism needs to have a platform for reaching many people. In some cases, participants described a platform as ensuring that information can reach people for whom it is relevant. In other cases, having a platform provided a form of accountability for information, subjecting information to the scrutiny of courts, regulators, and social media fact-checkers.

“They can’t just put things out there that are not true because they’ll get sued, obviously.” (South Africa)

Working for a recognized news organization can provide both of these benefits, for some participants. 

“Reputation is huge to me. Anyone can (and does) post to social media. Compare that to your Politicos that does actually have standards and doesn’t just sell out to whoever bids the highest.” (United States)

“A journalist is someone [who] will give, where, what, why and what.  He knows, ‘by the time I put that article on the internet, I need facts because if something comes up, my boss is going to put me in the firing line.'” (South Africa)

However, many more participants described a changing landscape in which many independent creators have access to platforms that provide the kind of reach that matters.

Beyond definitions

This essay is the first in a series of insights drawn from these focus groups. Even as CNTI uses the full focus group discussions to inform a much larger quantitative survey in the fall, we felt it was worth sharing some insights now. Forthcoming insight pieces will explore participants’ thoughts on uses of AI in journalism, the role of technology in getting informed, and decision-making around who to rely on and how to verify information.

About the Defining News Initiative

The Defining News Initiative is an 18-month effort that seeks to understand how concepts of journalism, news and information access are being defined in countries around the world. In three different realms — in legislation, among the public and among journalists themselves — our research and analyses will provide clarity and insight on the importance these definitions play in safeguarding an independent news media, freedom of expression, and the public’s access to a plurality of news in ways that inform policy discussions and decision-making.

How We Conducted This Research

CNTI contracted with Langer Research Associates to recruit participants for a combination of virtual — synchronous and asynchronous — and in-person focus groups, and focus groups moderators, in four target countries: Australia, Brazil, South Africa, and the United States.

These countries were selected strategically to capture geographic, cultural, and political contexts, as well as different news environments. Our recruitment efforts involved a screening questionnaire that asked potential participants about their information-seeking interest and behavior, prioritizing, but not exclusively relying on responses from individuals who reported that they keep up with events and issues of the day in some capacity. We recruited a total of 89 participants from these four countries (22 in Australia, divided into 2 groups; 25 in Brazil, divided into 2 groups; 29 in South Africa, divided into 3 groups; 15 in the U.S.), which we conducted between June 3 and June 7, 2024. In our recruitment efforts, we were intentional about maintaining diversity based on gender and age.

Recruitment and focus group discussion materials were designed by CNTI researchers and were reviewed by Langer Research Associates, local vendors and others with research and subject matter expertise. All focus groups in Brazil were conducted virtually in Portuguese and one focus group in South Africa was conducted in-person, with participants conversing both in Zulu and English. For focus groups conducted in languages other than English, such as the ones in Brazil and South Africa, transcripts were translated into English. 

The post Focus Group Insights #1: What Defines News, Journalism and Journalists appeared first on Center for News, Technology & Innovation.

Overview

The digital transformation of our news systems has, among other things, led to an “unbundling” of news information and the upending of journalism’s financial model in countries around the world. Many news organizations have been struggling to find a new model alongside continued newsroom layoffs and emerging news deserts. 

In response, a number of legislative efforts worldwide aim to bring more funding to the journalism industry and a rebalancing of digital revenue recipients. Many recent efforts have focused on new financing streams, mainly provided by either digital platforms — who now reap more than 50% of global digital advertising revenue — or, to a lesser degree, governments.

In an effort to protect the journalism industry, authors of this legislation seem to be attempting three main goals: (1) establish funding sources for digital journalism, (2) define specific types of journalism worthy of preservation and (3) contribute to a more robust digital news environment that benefits the public and functioning, free societies. 

When reviewed as a body, these legislative efforts display inventiveness and energy  in creating possible solutions for journalism’s future.  At the same time, while remuneration streams to support journalistic reporting are a critical component of a sustainable digital news and information environment, it is important to closely examine how legislative efforts around finance may impact other critical elements. 

This study examines 23 recently enacted or proposed legislative efforts from 2018 through 2024 aimed at providing revenue streams for journalism. We hope it offers a fulsome method for analyzing possible paths forward. There are two main parts of the report:

Part One groups this legislation into seven models for financing journalism. The financing models are organized around legal mechanisms that range from an expanded view of copyright to direct support for news by platforms and governments:

Part Two looks at how this legislation impacts other issues critical to a sustainable news ecosystem that supports functioning, free societies. We first address an implicit yet inconsistently treated concept that emerges from this legislation: appropriate compensation, if any, for various uses of (and interactions with) digital content. This includes the notion of setting legal parameters for proper compensation that goes beyond traditional definitions of copyright. We then look at how these financially-oriented legislations impact issues within other core aspects of journalism.

Area A: The Concept of Digital Usage in Legislation

• Issue: When Is It Appropriate to Charge for Digital Usage?
• Issue: Is Compensation for Digital Usage Consistently Applied?
• Issue: Who Benefits from Appropriate Compensation?

Area B: Balancing Financial Streams with Core Elements of Journalism

• Issue: How Can We Sustain Diverse Journalism?
• Issue: How Can We Sustain Independent Journalism?
• Issue: How Can We Sustain Journalism that Serves the Public?

Conclusions

While this body of legislation seeks to provide important financial lifelines to journalism, they also reveal several areas that require further consideration in order to create a sustainable news environment that enables an informed public. Specifically:

1. Reviewing recent financial models around the world brings to light the serious questions at hand about what a sustainable news media means and what it will look like in the years to come. The funding mechanisms — ranging from ancillary copyright and platform support to tax credits and hazard taxes — each stress different elements and set different precedents.  These approaches demonstrate the inventiveness of many who seek to shore up journalism, which is good to see. Yet they also pose issues and questions:

• Many pieces of legislation are proposed as temporary or triage measures to save journalism. As such, they can lack a holistic understanding of what the legislation (individually or overall) means for the flow of news information and how it should be managed long-term. This may pose problems should pieces of legislation become renewed without reflection.
• The continued development around financial models offers policy makers and stakeholders a broad array of possibilities to consider in enabling sustainability, and poses a question: how can the  news environment be made sustainable? If these efforts are stop-gap measures, what should a sustainable news ecosystem look like after they are complete? If some of these efforts are not emergency measures, how well do they fit in with other elements of the ecosystem?

2. It is important to address parameters around the use and sharing of digital content, but the way this legislation has begun to define it is problematic. This set of legislation presumes there is a point at which it is appropriate to require compensation for certain digital uses of and/or interactions with content. The legislation defines that point differently and in ways that create challenges given the wide range of how digital content is accessed and shared. This is a critical construct to get right as it will have a far reaching impact. It both requires and deserves more focused deliberation. Within this legislation there is:

• Disagreement about whether basic digital interaction amounts to usage.
• Inconsistent handling and application of how digital elements such as the URLs/links and small “snippets” of content should be addressed.
• A disconnect regarding the exact beneficiaries of this proposed digital usage, whether it should be journalism organizations, journalists or any creator of content (news or not).
• No consideration of how parameters would be applied beyond the scope of news publishers and specific large technology platforms.

3. A healthy news ecosystem requires a diversity of journalistic orientations, styles and innovations to serve the full public. Protection and further advancement in this area deserves top-level attention in legislative deliberations. The ease of digital content creation provided an entryway for smaller, independent, creative and minority-focused journalism and a way for the public to access it. A piecemeal approach to supporting diversity in journalism risks taking steps backwards in this regard. Within this legislation:

• Some legislation protects midsize and larger organizations, whereas others focus on smaller entities, and the amount of money that would be distributed across the spectrum is often not clear. 
• Legislation modeled around usage fees and licenses have limited references to minority ethnic media, while certain required negotiation approaches and state-funded efforts offer some explicit protection.
• The tax credit and data extraction models are the only models that explicitly define and cover local journalism. 
• Only the EU Directive and New Jersey’s consortium bill explicitly speak to innovation and keeping an eye on what developments might arise in the future.

4. This legislation as a whole raises questions about journalistic independence which should be directly addressed, especially in a global environment of declining revenue and press freedoms. Legislation around digital usage aims to lessen news organizations’ dependency on technology companies. However, it is not clear whether legislation could actually increase that dependency in certain ways. Legislation also inherently increases opportunity for the government to become involved in financial remuneration for journalism entities, which makes it important to be sure journalistic independence is overtly protected. More specifically:

• There is still uncertainty about specific details of implementation when it comes to governmental oversight or the implementation of legislation within the required negotiation and usage fee models. 
• Models designed around required negotiation, platform support and data extraction raise some concerns about government involvement in the mechanisms for distribution of journalistic content.
• Some of the legislation that focuses on platform support, local usage fees and hazard taxes may actually increase news organizations’ dependence on platforms even though they intend to mitigate it.

5. These legislative efforts, even when naming the public as ultimate beneficiaries, do not fully consider how to serve the public, including how the public stays informed and the kinds of journalism it values. References to the public are made throughout the legislation we reviewed, but there is little explicit explanation of how these steps will meet the public’s information needs and interests. The purpose of providing revenue streams for journalistic content — enabling an informed public and functioning, free societies — will be lost if the public does not access or value that content. Our analysis finds:

• Sometimes specific topics believed to be of interest or relevance to the public are specified for revenue support in several of the models (e.g., ancillary copyright, required negotiation and usage fees) but not all. 
• Concerns about public data and privacy emerge in the usage fee and data extraction models. 
• Withdrawing information from technology platforms risks less public access to information.

6. The evolution of media remuneration legislation has brought some improvements, but both journalism and the public can be better served if   those involved in discussions more thoroughly and proactively evaluate the legislative options. Legislative approaches have been adapted in response to problems resulting from earlier iterations and to the different needs of the journalism industry in the contexts of particular countries or states. And in several cases, the deliberations have resulted in non-legislative agreements negotiated among the government, parts of the news industry and technology platforms. This includes for example, the emerging outcomes of negotiations around California bills AB 886 and SB 1327. Whether resulting in formal legislation or not, a more beneficial approach to policies addressing news sustainability would be to think through their goals, options and potential unintended impacts with a more global and holistic perspective.

So, what is the most effective approach, legislative or otherwise? How can we make sure enacted solutions sustain the kind of journalism and information environment that functioning, free societies need? CNTI does not lobby or propose specific legislation and instead is dedicated to helping surface these answers through further research and collaborative, multi-stakeholder discussions, which we look forward to advancing in the months to come with a few discussion guideposts offered at the conclusion of this report. 

Find more details about this study’s methodology and data here.

Part One: Legal Mechanisms for Journalism Financing

As the first step in this analysis, we identified seven core legislative models of journalism remuneration that have been put forward which use different legal mechanisms for financial streams. We lay out each model and note cases where pieces of legislation have built off of each other over time. As bills are proposed and others amended in an effort to sustain the news ecosystem, these models offer a framework for evaluating their potential benefits and risks.

Different from broader frameworks, this analysis is tightly focused on specific bills and their structures of financial remuneration. We have focused on efforts made between 2018 – 2024 including many state-level bills in the United States. We did not capture measures related to direct governmental advertising spending or incentives and regulation regarding the ownership of news organizations, but we included links to these efforts in the methodology. There are also ongoing antitrust cases against technology companies as well as aspects of public policy in the civil society ecosystem, such as the US 2019 Covid Relief Bill, that can have a material impact on news finances, but are outside the direct scope of this research.

Some laws and bills apply more than one model. For example, a draft bill in Brazil includes language about bargaining which stemmed from Australia’s bill in 2021. Hybrid legislation has been marked with an asterisk (*). In addition, while some legislative examples may not have been enacted, they often remain under consideration so have been included in this report.

Model 1: Ancillary Copyright around Content

Legal Mechanism: Payment for the “use” of copyrighted materials, including journalism, when online on a mass scale

Financial Structure:

Journalism Defined/Protected As: Copyrighted content or copyrighted publication in any form; hyperlinking is exempted

Legislation with this model: European Union [2019/790] (2019, enacted); Brazil [PL 2370] (2023, proposed)*

An ancillary view of copyright is based on the premise that content made available online, such as journalism articles, are being utilized digitally in ways not well captured in earlier or existing copyright law. According to this argument, content creators such as news organizations are not being adequately compensated. Therefore, laws and policies that follow this model attempt to expand the ways that digital platforms, particularly large-scale ones, make use of content even if they are not necessarily quoting directly from that content.

The European Union’s (EU) 2019/790 Directive exemplifies this model. Before its enactment, several efforts across Europe attempted to expand copyright. For example, Germany’s 2013 Leistungsschutzrecht für Presseverleger (Ancillary Copyright for Press Publishers) allowed for short quotes of undefined length or “snippets” of news to warrant compensation. Though the German law was enacted, attempts to leverage the law were unsuccessful in practice. Publisher Axel Springer restricted the snippets from Google in 2014, for example, causing a plunge in traffic to its news sites. Springer lifted its restriction within two weeks.

The 2019 EU Directive has taken a slightly different approach. Sensitive to potential issues of copyright overreach, the Directive makes clear that elements such as URLs are not in themselves copyrightable; it also directs states to include text and data mining exceptions or limitations. Very short extracts also appear to be available for general use, though the Directive does not define “short.” In addition, in recognition of the ways online environments work, the Directive seeks to protect the open sharing of scientific, cultural and non-commercial information and does not mandate compensation for solely indexing of or access to that content. The Directive has enabled protections around the online use of content for 2 years (Article 15, para 4) and thumbnail images and short texts can be considered copyrightable. Due to EU procedures, some member states have yet to define and enact their own laws, yet this Directive has resulted in agreements between publishers in Germany, France, Spain and others.

There is vagueness in the Directive regarding exactly what content is being protected, a vagueness echoed in recent efforts to create a similar law in Brazil. Brazil’s PL 2370/2019 is still undergoing debate and revision, but at the time of this report, it has been encouraging negotiation between larger commercial platforms (with over 2 million users in the country) and creators of journalistic content whenever “elements, summaries, or […] other tools to expand any information present” are employed or added.

Model 2: Required Negotiation with Businesses

Legal Mechanism: Negotiation support for news organizations given digital platforms’ broad background access to news content

Financial Structure:

Journalism Defined/Protected As: Content or publication/organizations (US: journalist*)

Legislation with this model: Australia [Act No. 21] (2021, enacted but no designees); Brazil [PL 2370] (2023, proposed), Canada [C-18] (2023, enacted); New Zealand [GB 278-1] (2023, proposed); United States [S. 1094] (2023, proposed); possibly also California [SB 1327] (2024, proposed)*

Efforts are being made to codify an expanded notion of what using the news in an online context might mean, beyond using direct quotations or an individual reading an article. Beginning in Australia, several laws and bills avoid the creation of new taxes or direct government involvement in payment by instead requiring negotiation (“bargaining”) or arbitration between publishers and platforms that carry news. The result is direct payment from platforms to businesses (though to date terms have been negotiated outside the mandatory framework).

Different from the Model 1’s copyright approach around content, the required negotiation agreements focus on the activity of “availability” or “access,” which encompasses the work that digital platforms undertake to acquire, crawl, or index information in order to make that information available online. In addition, this legislation tends to consider elements such as URLs and snippets of news articles as worthy of compensation (though existing copyright laws are meant to remain in place). In the New Zealand bill, making content available includes “when any part of the content is reproduced on the digital platform or when the digital platform facilitates access to the content by linking to it.”

All of these pieces of legislation define conditions through which bargaining, or ultimately arbitration, must take place between large platforms (e.g., Australia’s “significant bargaining power imbalance” or over 50 million users monthly in the case of JCPA) and news organizations, the definitions of which vary. Brazil’s effort, having been inspired by Australia’s Code, also focuses on the negotiation between news and technology companies. 

In theory, this set of legislation leaves the precise terms of the remuneration to the negotiation, designed in part to allow for the possibility of collective negotiation. This exemption from antitrust enables news companies to organize. However, should a platform decide to no longer carry news, the requirement to negotiate may not be possible to enforce; in Australia, Meta has said it will drop news from its platform should the minister try to designate it under the Code. The law and its enactment can also be different, as the case with Canada has shown (discussed further in Model 4).

Model 3: Local Usage Fee around Link Distribution

Legal Mechanism: Platform payment for the local public’s direct access of journalism content, such as through clicks, or payment for locally relevant content

Financial Structure:

Journalism Defined/Protected As: Journalists, who are defined by employment time and tasks, within news organizations

Legislation with this model: United States – California [AB 886] (2024, not passed)*; United States – Illinois [SB 3591] (2024, proposed)

In contrast to the open-ended negotiating frameworks of Model 2, recent approaches in the United States have sought a more straightforward assessment or quantification of digital usage around distribution or links. Examples of payments to publishers based on the number of links can be found in two state bills, originally using virtually identical language: the California Journalism Preservation Act (AB 886), 2023 version and the Illinois Journalism Preservation Act (SB 3591), whose language copies much from the earlier California example.

Illinois’s proposal (which was very similar to California’s proposal prior to amendments to the CA bill on June 10, 2024) states that liable technology platforms must “track and record” the number of times an eligible journalism provider’s content is linked, presented or displayed to residents of the state on a given digital platform. The proposal further specifies how the usage fees will be allocated: one percent of the usage fee will go to “digital journalism providers that produce 30,000 annual search occurrences in Illinois searches or 10,000 annual social media impressions from Illinois.”

The bills using this funding mechanism have been criticized for possibly fueling clickbait and misinformation. Against these critiques, lawmakers revised California’s proposal in June 2024 in ways that deeply altered its structure as well as its language. Now instead of compensating for clicks, the bill attempted to outline strong reasons for online platforms to bargain with local news organizations – making it more similar to Model 2 in many ways. “Usage” was redefined as more generalized access to the content produced by journalists and the compensation model is much vaguer; the precise calculations evaporated from the California example. California’s latest version of the bill appears however to have been pulled off the table, in favor of a deal with Google and others that, among other elements, may provide newsrooms with nearly $250 million in support (a portion of this total is for an “AI Accelerator” which is not journalism specific).

Model 3 is noteworthy because these examples of legislation move away from defining journalism organizations alone and instead focusing on the professionals who work for them. The new version of California’s bill attempted to compensate news organizations with journalists — including freelancers — who produce information that is of relevance for “local California audiences,” though how that relevant access or usage will be measured outside of clicks was not clear. 

The financial support in both of these bills (CA and IL) is structured to come from large digital platforms such as Google and Meta. Qualifying platforms are defined by their national user base (possessing 50 million United States-based monthly active users or subscribers) or national yearly revenue (550 billion USD in sales or adjusted market capitalization).

Model 4: Platform Support for News Organizations

Legal Mechanism: Platforms as financially responsible for a new digital environment in which the news industry cannot produce and distribute quality journalism

Financial Structure:

Journalism Defined/Protected As: Press organizations

Legislation with this model: Canada [C-18] (2023, enacted)*, Indonesia [Decree No. 191884] (2024, enacted)

Indonesia’s legislation states that digital platform companies have a responsibility to support “quality journalism” by cooperating with press organizations. Examples of collaboration between platforms and press companies include paid licensing and profit sharing. However, the regulation lacks specific details in several of the financial support provisions.

Inspired by the Australian News Media Bargaining Code (Model 2), Canada’s C-18 law did not initially involve the government serving a central role. However, the development of C-18 departed from Australia’s example in several ways. The implementation of C-18 led to a negotiation between Google and the Canadian government, rather than between companies. For this reason, Canada’s example now seems closer to requiring direct platform support of news organizations than the other legislation in Model 2. Meta, on the other hand, responded by removing all news organizations and links so that the law would not apply. Google threatened a similar removal of links prior to the negotiated agreement with the government.

It is unknown how these policies would fully work in practice. The Press Council in Indonesia, Dewan Pers, is a third-party industry association that is expected to have a large role in determining the outcomes and processes of how platforms support news companies in the country. As of July 2024, the Canadian Journalism Collective was still working out issues of governance with requirements for diverse representation among its directors.

Model 5: Government Tax Credits

Legal Mechanism: Tax credits for local news, which citizens need access to

Financial Structure:

Journalism Defined/Protected As: Local for-profit newspapers, journalists

Legislation with this model: Canada [Canadian Journalism Labour Tax Credit; Digital News Subscription Tax Credit] (2019, enacted); United States – Virginia [HB 1217] (2022, not passed); United States [HR 4756] (2023, in progress); United States – Washington State [SB 5199] (2024, enacted); New York [A2958C] (2024, enacted); Wisconsin [AB 1140] (2024, not passed); Maryland [HB 540] (2023, in progress); Illinois [SB3592] (2024, enacted)*

This model exemplifies efforts to leverage tax credits as a way of supporting local news. Justification for the use of public monies for this purpose hinges on the importance of local news for citizens to be fully engaged in, and make decisions about, their communities. 

Depending on the context, tax credits can be non-refundable, providing an exemption or “break” for qualifying businesses. They can also be refundable, potentially amounting to additional funding for particularly small organizations. Both types of credit can be seen in Canada, whose efforts became effective in 2019. The Canadian Journalism Labour Tax Credit offers a 25% refundable tax credit on salaries and wages while the Digital News Subscription Tax Credit offers all individuals a non-refundable personal tax break on subscriptions to qualifying organizations.

There are many ways that tax credits have been and are being considered. First, tax credits are being offered directly to journalism organizations. Examples include straightforward tax relief, as with the United States House of Representatives’s Community News and Small Business and Support Act (NSBSA), Virginia and Washington State. Examples from New York and Illinois offer refundable tax credits. New York’s law offers financial support to news organizations for the compensation (and retention) of journalists through tax credits, some percentage of which is refundable; It should be noted that the law benefits print newspapers explicitly, while non-profit, TV and radio organizations do not currently qualify. A similar situation exists in Washington State, where the emphasis is on print newspapers; local digital-only sites do not qualify. 

Tax credits can also be considered for other companies, incentivizing advertising in local journalism. Maryland’s example focused on costs associated with advertising incurred by small to midsize newspapers. A similar effort is underway within the NSBSA and Massachusetts; Illinois recently passed a state budget that not only included $25 million of refundable tax credits for local journalist wages and funding for college scholarships (making Illinois’ case a hybrid with Model 6/government grants), but also tax credits for local businesses advertising in local newspapers. 

Finally, tax credits can be offered directly to consumers themselves. Wisconsin initiated an effort to provide tax breaks to its residents for local newspaper subscriptions. In its current iteration, the bill did not pass the State Senate in April 2024. 

Because this funding mechanism works within existing tax codes, no special oversight authority has been identified as required in either bill.

Model 6: Third-Party Government Grants

Legal Mechanism: Government support of local news through grants administered by third-parties, such as universities and consortia

Financial Structure:

Journalism Defined/Protected As: Local journalism efforts or developing journalists in local newsrooms

Legislation with this model: United States – New Jersey [A3628] (2018, enacted); Canada Local Journalism Initiative (2019, enacted); California [AB 179] (2022, enacted); New Mexico [SB 159] (2023, not passed); [SB 57] (2024, not passed) — though related governor-approved appropriations in both years; Wisconsin [AB 1139] (2024, not passed); Illinois [SB 3592] (2024, enacted); California [SB 1327] (2024, in progress – uncertain)

As opposed to Model 5 (tax credits), government grants actively give funding to more broadly defined journalistic efforts. 

Because this model delegates decision-making to a consortium or other third parties, these laws do not include precise definitions of journalism content, organizations or individuals. This may allow grantors more creativity and flexibility in determining which efforts might benefit from funding. However, in cases where funding explicitly goes towards fellowships, it is clearly supporting workforce development within local newsrooms. 

The amount of funding distributed through this mechanism depends on how much the governments designate. The level of funding can often be much lower than what is provided through the other models discussed in this report. As of 2024, the New Jersey Civic Information Coalition, founded in 2018 through State Bill A3628, has supported 81 projects with $5.5 million (USD) while Canada’s Local Journalism Initiative has given out $50 million (Canadian dollars) since 2019 and has recently committed $58.8 million more. California’s bill contained both types of financial support, with $25 million (USD) designated to the University of California at Berkeley to support local news fellowships and $5 million in grants for ethnic media.

Model 7: Hazard Tax by Government upon Platforms

Legal Mechanism: Platforms extraction of (local) citizens’ data amounts to a general hazard that requires remuneration

Financial Structure:

Journalism Defined/Protected As: Local journalists/journalistic service providers

Legislation with this model: United States – California [SB 1327] (2024, in progress – uncertain)*

California has the only bill that fits within this model. This hazard tax (which has some significant distinctions in structure and operation than typical taxes — see the language of the bill for more) is supported by two justifications. The first is an understanding that large internet corporations are collecting data about people in order to advertise products and services to them. Because people exchange their data for access to the products and services, this “barter” is not captured within typical sales tax structures. The second is a recognition that journalism — especially ethnic media — performs a critical role in functioning, free societies. Implicit here is that advertising has been a critical part of journalism’s sustainability model (Section 1 of CA SB 1327).

Possibly due to these two purposes, there are two mechanisms for funding. Funding from large corporations would be obtained directly at the sales tax rate (7.25%), the results of which go into the “Data Extraction Mitigation Fee Fund.” This revenue eventually benefits qualifying journalism organizations after satisfying fundamental requirements such as school funding (Section 4). In addition, not only can qualifying organizations obtain credits against the taxes that they owe the state, but smaller organizations can also gain funding because it is structured as a refundable tax credit. Different from Model 5, tax relief is provided by the platforms so that there is no loss of revenue to the state. Furthermore, extra revenue paid by the platforms may directly fund organizations’ local news endeavors.

The bill’s author, Steve Glazer, likened his effort to taxes that mitigate environmental hazards. Though any internet corporation that employs the citizen data barter for targeted advertising might be held accountable, the bill only seeks remuneration from very large organizations; the amounts potentially owed by smaller technologies are regarded as “negligible.” 

The legality of this effort is currently in question and may be challenged constitutionally based upon past decisions such as Grosjean v. American Press Co., 297 U.S. 233 (1936) and Minneapolis Star Tribune Company v. Commissioner, 460 U.S. 575 (1983). However, the recent negotiation among the State of California, Google and others that replaced the CJPA (Model 3) may also mean that Glazer’s bill will not move forward. 

Part Two: Key Issues for a Sustainable Digital News Ecosystem

All legislative approaches under consideration attempt to address a critical requirement for the future of journalism: regular financing streams. They cannot and do not aspire to address all aspects of a sustainable digital news ecosystem. In fact, several of these measures have expiration dates or requirements for renewal, highlighting their stopgap or temporary approach to finding funding solutions for the news industry. 

Yet laws can create long-lasting effects, which can only be mitigated through a thorough analysis of potential risks and tradeoffs. The justification for requiring payment from platform companies hinges on an emerging premise: the journalism industry, and with that the digital news ecosystem, may be sustained by charging for the digital “usage” of journalistic content by technology companies. Other approaches described in the models suggest that sustaining the news ecosystem may involve more compensation from governments.

Setting aside the ongoing debate about whether news providers can generate financial sustainability themselves, the following section discusses several issues related to the rationale behind the funding mechanisms. The first set focuses on the notion of compensation-worthy digital interactions beyond what a reader, consumer, or end user might also need to pay. The second set focuses on how these legislative examples handle the needs for diverse and independent journalism and journalism that serves the public.

The Concept of Digital Usage in Legislation

Across five of the models, the notion of usage encompasses the different ways that digital content is made available to the general public. Usage also applies to the technical means by which digital platforms process news information to make it available to a wider audience. For this version of usage, legislation such as Canada’s Act C-18 seek “fair compensation to the news businesses for the news content.”

“Use” and “usage.” Several pieces of legislation discuss the terms “use” and “usage” to describe different interactions with digital content.

• Examples from Models 1 and 2 focus on publications and content, particularly how they are made available via digital technologies:
  • EU Directive (2019), Article 15 “Protection of press publications concerning online uses.”
  • Brazil (2023): Section III-A; Article 21-A. “Journalistic content used by digital platforms for third-party content [Os conteúdos jornalísticos utilizados pelas plataformas digitais de conteúdos de terceiro] that have more than 2 million users in Brazil, produced in any format, including text, video, audio, or image, will result in compensation to the legal entities that produce journalistic content.”
• Examples from Model 3 focus on the sharing of that content through links:
  • California (2023 version): “journalism usage fees”; “journalism usage fee payment.”
  • California (2024 version): “Title 23. Compensation for Journalism Usage.”
  • Illinois (2024): “Section 15. Notice requirements for journalism usage fee payments.” 
• Model 7 focuses on digital use through the data collected by end users or news consumers.

“Access” and “availability.” Though examples from Models 2 and 4 do not always employ the explicit term “usage,” their descriptions of digital “access” or “availability” mimic the kinds of interaction captured in the other models.

• The Australian and Canadian examples employ the terms availability, as well as interaction and (in Australia) distribution. 
• In the US JCPA, “use” may be distinct from “access.” The term is tied to a series of activities that are not the subject of the bill (e.g., “no antitrust immunity shall apply to … the use, display, promotion, ranking…”). 
• New Zealand’s Bill (2023) employs both the terms “availability” and “use”: “Supporting the efforts of New Zealand news media entities to secure revenue for the use of their content online will provide a critical revenue stream and mean that the sector will not be reliant on government funding in the future.”

It is also worth noting that while this legislation and this report center on revenue lifelines for journalism, questions around fair compensation and usage are also at the heart of other policy discussions related to journalism and technology, such as the use of content to train generative AI models.

Issue: When Is It Appropriate to Charge for Digital Usage? 

Exactly what digital usage of news information warrants compensation? Significant portions of journalism’s content are now being distributed through digital platforms which offer far less industry control than earlier distribution methods like print. Recent legislation reflects various ways lawmakers are addressing the question of compensation for digital usage. 

Much of this legislation aims to overturn common understandings and practices on the internet that — due to the protections of open knowledge and public exchange in many democratic societies — allowed many aspects of an article’s content, such as its URL, title and short summaries, to be referenced, shared, accessed or “used” without compensation. This is because ideas, information and facts are not copyrightable. Thus, there are questions that need to be addressed in order to isolate whether the concept of appropriate compensation around digital usage can support journalism moving forward and, if so, how.

Usage or Discovery: Determining Link Value. As researchers and others seek to quantify the significance of technology platforms providing access to news, there is a critical question at hand: What precisely is the value of facilitating digital interactions by linking to news? Is it usage, discovery or both? Much remains unclear.

• The News Media Alliance and Initiative for Policy Dialogue argue that large platform technologies such as Google and Meta gain significant financial benefits from the inclusion of news information. On the other hand, Meta’s decision to remove news from its platform in Canada and to consider removing news from the Australian market suggests that the financial gains may not be significant.
• Others argue that providing access is a promotional service to news organizations — like a newsstand or grocery aisle with publications — allowing their content to be discovered; sharing information via aggregators may draw people to news content more. In fact, a recent study has shown that the majority of Canadians appear not to notice that they have lost professional news content — not only are they not visiting traditional or ethically-minded news sites, they are now using the platform to get a more biased and less-factual kind of news.
• The recognition that technology platforms provide valuable traffic to news organization content has led to the inclusion of non-retaliation clauses within many agreements so that platform companies do not willfully affect that traffic. However, non-retaliation agreements may suggest that, in order to ensure the appearance of compliance with the law, technology companies are now required to carry all news providers. The requirement to carry news content may fundamentally change the purpose of certain digital products. It may also negate the concept of fair compensation for digital usage altogether if a technology has no choice but to provide it. There is some debate on this issue.
• If “usage” equals “the digital processing of content,” what constitutes digital processing varies depending on the result or product. In some instances, small elements of metadata allow for discovery of news information without computation. But, other kinds of digital interactions for various news products (e.g., automatic news summaries) likely require more processing of the original text or content. With the incorporation of generative AI, how we define “computational usage” is also important.

Diminishment of Open News, Expansion of Copyright? Charging for digital usage expands copyright and producer or author rights in ways that raise questions about the open sharing of news in a digital context. Compensation for digital usage may also be illegal in certain contexts.

• The question of whether “access” qualifies as compensation-worthy usage poses a problem for a context like the United States where the “fair use” of information exists. Fair use is a way that copyright-protected works can be used without compensation in certain contexts that protect the public’s freedom of expression and incentivizes creativity in a way that benefits society.
• Moreover, within the United States, legislation that impinges on the curation or moderation of third-party content is eligible for First Amendment protection; the Supreme Court’s recent Moody v. NetChoice (2024) decision rejected any must-carry obligations. 
• In contexts where author rights exist, such as the EU and Brazil, we see the actual or potential removal of exemptions for news content. Take, for example, the Copyright and Information Society Directive 2001 (2001/29/EC), which was amended by the 2019 Directive reviewed in this analysis. The Copyright and Information Society Directive used to explicitly provide for exceptions and limitations to copyright in EU states when it came to making news available (Section 5). 
• Either way, the information and facts of news reports themselves are not copyrightable. This makes the effort to define compensation around digital interactions particularly thorny. Exceptions to copyright and author protections are also made in the name of education and scientific progress. This may also be why a key metric in the EU Directive regarding digital interactions with news content has to do with time (2-year expiration). Despite efforts to limit their impact, there is a risk that the expansion of these rights lessens incentives to use up-to-date information.

Impact on Paywalls. If consensus about fair compensation for digital usage is achieved, should this have an impact upon content paywalls? For example, if local news organizations gain tax breaks or subsidies from the government — which ultimately come from taxpayers — does this require some of that news to be freely accessible to the public? 

Issue: Is Compensation for Digital Usage Consistently Applied?

Beyond any questions regarding the principle itself, there is still no clear consensus as to how compensation for digital usage is applied either when considering which elements of digital content qualify or how to calculate the compensation. This lack of consensus is an indication of the difficulties behind the overall concept of compensation for digital usage.

Variance in Applicable Components. A number of components in news content — such as URLs/ links and quotes — are being directly addressed or indirectly referenced by this body of legislation. However, the different pieces of legislation do not always agree with each other about the treatment of these specific elements.

• URL links 
• Article titles
• Very short texts and text snippets
• Larger text quotes 
• Article summaries
• Usage of text and audiovisuals for indexing
• Usage of text and audiovisuals for generative products
• Information about the event or fact being reported on

Inconsistent and Limited Application. Compounding the lack of clarity around which elements of news content justify compensation, there are also inconsistencies in application. Though usage is defined, the formulas for compensation vary and it is not being applied to every organization or entity who might qualify. These inconsistencies undermine the usage argument.

• For example, there is an inconsistency in the metrics and definitions of usage rate: while some laws focus on the amount of content being used as a way to create the formula for compensation (Models 1 and 3), others look at the news costs for an organization, no matter how much content it may generate (Model 5). The formula is not always transparent, as with Australia’s agreements which are confidential (Model 2).
• There is a risk that sensationalist and malign reporting could be remunerated in some cases. In Australia (Model 2), there are ethics tests that news organizations must undergo to prevent the funding of sites that do not engage in editorial review or fact-checking. However, this type of review does not exist across the board (e.g., the controversies around the earlier version of California’s AB 886).
• Most of these proposed statutes also focus on defining applicable usage fees for a specific group — only very large platforms qualify (such as platforms with at least 50 million impressions per month or “third-party digital content platforms that have more than 2 million users”). These efforts are an attempt to rebalance the flow of digital revenue away from the companies earning significant monies from advertising. While the focus on larger companies may provide access to larger sums of money, it does not fully define what is worthy of compensation regardless of the user. For example, should news organizations themselves — many of whom have also created technology platforms for themselves that share external links — fall under this “appropriate compensation” concept?

What content is free and what deserves compensation?

Issue: Who Benefits from Appropriate Compensation?

While this body of legislation makes the argument that digital usage warrants compensation, it is not yet clear what exactly that compensation should amount to or who should benefit from it. The financial mechanisms included in many examples of legislation could apply to all content being aggregated and re-distributed on the internet. There is also the question of whether the results of these mechanisms are fair to the public.

Within these financial mechanisms, there are inconsistencies and limits to who may qualify. The various legislative efforts approach the challenge of identifying and protecting legitimate beneficiaries differently, whether based on content, organization, professionals or some combination. With the intended goal of bolstering news sustainability, further consideration is warranted about who is covered and how news organizations may use the funds they receive.

Journalistic Content. Journalism topics, information or publication formats included within the legislation focus on current affairs and the public interest.

• The main orientation of Model 1, expanded copyright, is around the content. As such, the EU Directive does not include a definition for journalism or journalist, though it does include a definition for press publication. Compensation in Brazil’s example must take into account the amount of original journalistic content created, among other factors. Laws and bills from Model 2 tend to focus on the notion of news content dealing with current affairs and the public interest, with beneficiaries defined as the organizations (e.g., producers, broadcasters, businesses, entities, etc.) who produce this content.

Journalism Organizations. The journalism organizations (i.e., organizations that produce journalism or who employ journalists) protected by the legislation tend to be online, traditional and midsize or large, though there are sometimes explicit protections for small, local outlets especially in government-supported mechanisms. 

• In Models 2 and 3, pieces of legislation are expressly focused on midsize or larger organizations. For example, qualifying organizations are based on thresholds for revenue which include $150,000 (AUD) in Australia and $100,000 (USD) in the US (though the 3 largest US publishers are excluded from JCPA). An important question is whether hedge funds, which own news organizations, may qualify.
• For those in Australia and New Zealand (Model 2), organizational qualification checks include professional standards tests, audience tests and content tests. The legislation in Australia and New Zealand do not require any transparency about how the funds within an organization should be spent while the legislation crafted by the US (Model 3) and Canada (Models 2 and 4) added a transparency requirement.
• Under Model 5, Wisconsin’s attempt provides subscription credits to local news organizations, however, New York’s effort excludes consideration of non-profit and broadcast entities.

Journalists. Qualifying journalists are mostly defined by full-time work within a range of specific kinds of tasks within formally incorporated news organizations, though some legislation recognizes freelance roles.

• Perhaps in growing recognition that earlier legislative attempts may not support the actual production of news, Model 3 focuses on trying to ensure that journalists are themselves beneficiaries. Journalists are defined as humans or “natural person(s)” who engage in a number of news production activities such as “gathering, preparing… presenting, distributing, or publishing original news or information…” (AB 886) (though it is not entirely clear what type of original news gathering could qualify). Organizations then become qualified if they, for example, spend 50%-70% of the received revenue on journalists. The most recent revision to California’s bill also made sure to include freelance journalists.
• In Model 4, Indonesia’s regulation stipulates that “news is a journalistic work by journalists” in a context where journalists have official press cards.
• In the New York example from Model 5, a qualifying news journalist resides within 50 miles of the local news agency under consideration for the tax credit and must work at least 30 hours per week on qualified services (i.e., activities related to producing original news content). Model 7 focuses on outlining requirements such as the residence of full-time journalists, who are defined by activities such as “gathering, preparing, recording, directing the recording of, producing, collecting, photographing, writing, editing, reporting, presenting, or publishing original local community news for dissemination to the local community.”

Balancing Financial Streams with Core Elements of Journalism

Beyond the issues related to digital usage and compensation, we turn our attention to the main goal of the legislation: sustaining journalism so that it can ultimately fulfill its mission to support functioning, free societies. In finance-focused approaches, it is important to examine what might be missing and what might unintentionally introduce new risk to advancing this overarching goal. 

A range of values and ethical considerations are important for strong, high quality journalism. Particularly in today’s digital world, CNTI has laid out five components critical to a fully sustainable digital news environment: revenue to support journalistic work; journalistic independence; diversity of providers; technology to produce, distribute and receive news; and a public that finds journalism valuable and relevant.

This analysis considers three of those elements beyond revenue: (1) diversity, (2) independence from influence and conflicts of interest and (3) public service (including what is relevant to the public). A detailed examination of the language inside each piece of legislation related to these three elements can be found in this table.

Issue: How Can We Sustain Diverse Journalism?

One of the ways the internet has proved transformative for news is how it has diversified both the forms and producers of journalism. With Web 2.0, the world read, heard and engaged with many different perspectives on a range of topics, some of which had never received attention in mass media. As Canada’s efforts have noted, “citizens’ access and exposure to a diversity of content play a central role in the making of a resilient democracy.”

Within the legislation, we find little overall attention to journalistic diversity. The safeguarding of diversity includes aspects of journalism’s content, organizations and its professionals,  as well as dimensions such as ethnic media, those serving gender identity or other special communities and independent and freelance journalism. We selected a few to consider here.

Minority Ethnic Media. Some special considerations exist for minority ethnic media; however, it is not always clear whether and how these outlets will receive funds.

• Two of the examples from Model 2 consider contributions from minority media. Both Canada and New Zealand’s legislation also feature specific provisions for indigenous (Canada) and Māori (New Zealand) news media. These special protections for diverse news are not found in the Australian or United States legislation. While these protections are valuable, they miss opportunities for a more comprehensive consideration of what supporting minority media might entail. However, the administrators over Canada’s Local Journalism Initiative, mentioned in Model 6, may allow for more comprehensive consideration.
• Both the California and Illinois legislative drafts from Model 3 cite minority ethnic and, in particular, Black media’s importance: “Given the important role of ethnic media, it is critical to advance state policy that ensures their publishers are justly compensated for the content they create and distribute.” 
• Yet, it is not clear that especially small minority publications, though mentioned in the preamble of the California’s draft, would be rewarded. A number of small, local ethnic media do not meet the thresholds of revenue. As print publications, minority ethnic outlets also may not qualify since these legislative efforts are focused on digital distribution.

Local Journalism. Several of the models make reference to local news, with some examples of legislation specifically striving to protect local journalism organizations and the journalists who work within them. However, locality is a relative term, and is defined differently in the pieces of legislation that reference it. It would be helpful to consider the concept and language more thoroughly.

• Models 2 and 3 make reference to local news, but have no special provisions other than national or state-level registrations or physical locations.
• Models 5 and 6 focus on local organizations with local journalists, as described earlier, such as New York’s journalist residency requirement. 
• Model 6 also offers examples where grants and fellowships through universities or other organizations can support local journalism projects. 
• The aim of the California bill in Model 7 is to support local journalism, which mentions the consideration of small ethnic outlets. Local journalism is determined by the residence of full-time journalists who are defined by activities such as “gathering, preparing, recording, directing the recording of, producing, collecting, photographing, writing, editing, reporting, presenting, or publishing original local community news for dissemination to the local community.” Smaller outlets receive a larger percentage of funding relative to their size.

Innovation. While we can see some areas in which news diversity is being considered or protected, this is not the same as creating a context in which journalism might flourish. The stated goal of much of this legislation is triage: to simply give journalism a financial lifeline. Thus, the legislation overall does not consider the issue of continued developments within journalism or what might be required to remain flexible in the future. 

• The EU Directive within Model 1 and the New Jersey Consortium of Model 6 are the only examples within our analysis that approach the challenge of providing additional revenue while considering creativity and continued innovation in the digital marketplace. For example, the Directive notes that a “harmonised” legal framework needs to protect against the exploitation of works, thereby “stimulat[ing] innovation, creativity, investment, and protection of new content” while also allowing for limitations to education and also, potentially, text and data mining technologies.
• Copyright can aim, it has been argued, to protect diversity and robustness in journalism by rewarding the creativity of all outlets, including small ones, because it applies to all content rather than a particular type of provider. Whether ancillary copyright proposals further or diminish this diversity is a point of discussion.

Issue: How Can We Sustain Independent Journalism?

Independence from influence and conflicts of interest have long been a key value and ethical consideration in journalism. For some, usage payments from technology companies to journalism providers alleviate concerns about the dependence of the journalism industry on digital platform companies (such as to reach audiences or philanthropic offerings of money or technology tools). The creation of mechanisms that justify fair or reasonable compensation by technology companies may help to do this, but they require laws. 

With increased legislation, there are increased implications for governmental influence — especially long-term as government regimes change. Indeed, as CNTI discusses in other publications, press freedoms around the world are in decline across all government regime types, alongside increased government reach. We also explore possibilities where legislation may inadvertently increase dependence upon technology companies.

Government Influence in Financial Determination. When it comes to the implementation of these different pieces of legislation, questions arise as to when and how special bodies will exercise their oversight and whether they have a role in determining who receives compensation.

• Some of the legislation within Models 1, 3 and 5 do not outline special roles for government, relying instead upon the existing structures of courts for copyright and tax commissions for tax credits. However, there is governmental involvement in defining which organizations or individuals can qualify for tax credits to begin with. 
• Other legislation within Models 2 and 7 outline special roles for governmental agencies and regulators. These duties include designating companies who must negotiate, validating the eligibility of news organizations and enabling arbitration in negotiations with technology companies. Sometimes governmental involvement may be limited to refereeing arbitration. As currently worded, Brazil’s draft legislation includes details about the institutions responsible for overseeing fair negotiations (e.g., a Private Chamber of Arbitration or a body of the federal government (Article 21-A para. 9)). 
• Uncertainty exists regarding precisely how these pieces of legislation will be implemented in many instances. Though we know that the extra-governmental Press Council has been identified as the body of special oversight in Indonesia under Model 4, the details of this role have not yet been determined. And under Model 7, the California Franchise Tax Board suggests that certain organizations, such as ethnic media or smaller outlets, may gain priority in funding but it is as yet unclear how this will be determined.

Government Impact on News Distribution. The current legislative examples tend to increase the likelihood that news distribution will be affected. 

• Some legislation under Model 2 may include requirements for platforms to carry all purported news organizations indiscriminately, albeit that requirement may be unintentional. The “non-retaliation” stipulations within the required negotiation agreements are intended to ensure that platform companies do not attempt to take actions against organizations who wish to use the mechanism. However, as discussed earlier, platforms may consider themselves required to carry the content of all providers that identify themselves as news outlets, including propaganda or promotional sites, in order to not fall afoul of this law. This stipulation may now be unconstitutional in the United States after the Moody v. NetChoice decision. 
• In Model 4, Indonesia’s case is still undetermined, but seems to require platforms to enforce potential Press Council directives. Overall, much is still unknown about how Indonesia’s policy would work in practice. While the Press Council has been an important function of journalistic independence from the government, there is also evidence that its authority can be sidestepped.

Platform Influence on Journalistic Content and Funding. While this legislation seeks to empower news organizations through increased bottom lines, and offers increased opportunities for non-philanthropic funding from technology platforms, questions of platform independence, let alone greater dependence, still exist.

• In several of the models, it seems that platform influence may now be introduced in different ways. News companies are dependent upon technology companies to provide them a digital definition of locality in Models 3 and 6. In other examples, the legislation or implementation is simply unclear.
• Increasing the percentage of funds coming from technology companies can make a news organization literally dependent upon those companies, raising questions about possible downstream effects to that organization’s editorial decisions or other issues.

Issue: How Can We Sustain Journalism that Serves the Public?

A central need for functioning, free societies is an informed, engaged public. Thus, so is news that serves the public and a digital news environment that is openly accessible to them. The models of legislation in this analysis vary in how well they support these goals. While some references are made to serve the public interest, what that actually means or how these pieces of legislation do that is either vague or omitted. The legislative efforts also often neglect issues such as privacy or the risk of losing access to news in online platforms altogether.

The Public Interest (or Interests). When invoked, the meaning of the term “the public interest” is not always clear — it is sometimes “The Public Interest” in terms of the common welfare, but it at times is about topics the public is interested in or curious about. 

• On the one hand, the goal of supporting journalism that serves “local, regional, national, or international matters of public interest” is explicitly addressed in a couple of the models, particularly in Model 2. In the Australia example it is clear this includes what Australians consider to be important: “current issues or events of public significance for Australians at a local, regional or national level” or “issues or events that are relevant in engaging Australians in public debate and in informing democratic decision-making.” This attempt to define the content worthy of legislative support makes sense, but what if deep entertainment-related news is, in fact, what a certain portion of the public relies upon?
• On the other hand, the New Zealand proposal does include broader categories for relevant news content such as “communities that share other characteristics (including age, disability, sex, sexual orientation, gender identity, ethical belief, or religious belief); or (e) people with an interest in specific subject matters (including the arts, sports, science, health, business, or the environment).”

The Public’s Privacy. The issue of what is owed to the public when it reads news and when its data is used (or “extracted” in the term of the most recent California bill) is a question that not only platforms but also news organizations should answer.

The Public’s Access. Here we focus on the public’s literal accessibility so that one can digest key information that is relevant to know in free societies, particularly within democracies. Making journalism widely accessible is another area largely improved with the birth of the web and online distribution. Will legislation seeking to bring a lifeline to journalism providers inadvertently make it harder for people to access news they find relevant? 

Appendix: The Legislation

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