AI In Journalism Archives - Center for News, Technology & Innovation https://cnti.org/focus-area/ai-in-journalism/ Mon, 15 Jun 2026 13:52:42 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://cnti.org/wp-content/uploads/2024/03/cropped-favicon-1-32x32.png AI In Journalism Archives - Center for News, Technology & Innovation https://cnti.org/focus-area/ai-in-journalism/ 32 32 AI Applications in Investigative Journalism https://cnti.org/briefing/ai-applications-in-investigative-journalism/ Mon, 15 Jun 2026 13:00:00 +0000 https://cnti.org/cnti-news// The fourth briefing from the AI and Journalism Research Working Group finds that the individual nature of investigations is a challenge for adopting AI tools in investigative journalism.

The post AI Applications in Investigative Journalism appeared first on Center for News, Technology & Innovation.

]]> Introduction

Investigative journalism plays a critical watchdog role in holding power to account, and AI tools — ranging from automated document parsing to Large Language Models — are both enhancing and complicating that role. While AI systems can make investigations more efficient and scalable, they are not uniformly adopted due to constraints such as data quality, technical expertise, source confidentiality, economic resources and global inequalities. At the same time, AI companies and technologies themselves are becoming a subject of investigation, requiring journalists to scrutinize opaque algorithmic systems and powerful technology actors. Understanding these dynamics is essential for practitioners, researchers and industry leaders seeking to responsibly integrate new tools into investigative workflows while preserving journalistic integrity, independence and impact.

CNTI’s AI and Journalism Research Working Group examined how artificial intelligence (AI) is reshaping investigative journalism, drawing on a review of 44 recent academic and industry studies. This briefing synthesizes global research to assess both the opportunities and structural challenges AI introduces into investigative reporting.

About

This is the fourth 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 21 cross-industry members from around the world, bringing research, journalism and technology expertise to the discussions. 

The goal of the working group is to offer succinct summaries of global research in specific topics at the intersection of journalism and AI. Each quarter, the working group synthesizes the state of research across two to three topics for journalism practitioners, researchers and industry leaders around the world, focusing on actionable recommendations for journalism — not other fields that are concerned with AI.

In each report, we lay out the general findings of the research to date, considerations and/or actions for practitioners and areas where more or new research is needed. 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 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.

AI Applications in Investigative Journalism

Research we reviewed suggests
  • AI tools are transforming investigative journalism foremost by expanding its capacity to handle large-scale data and investigate complex systems. They also redefine what counts as evidence, what is investigable and how journalistic knowledge is produced.
  • AI tools do not replace core journalistic functions, which remain deeply human and context-dependent.
  • Adoption of AI tools is shaped by economic constraints, skill gaps, language barriers and global inequalities, as well as by newsroom hierarchies, organizational culture, digital divides and external infrastructures. Each investigation is somewhat idiosyncratic, which means that customized AI tools provide more value than off-the-shelf solutions.
  • Using AI tools in investigative journalism requires collaborative efforts that bring together people from diverse backgrounds and different areas of expertise.
  • AI itself opens a major new area of investigative journalism around holding institutions accountable for decisions made by AI systems. 

Investigative journalism has a distinct position within the news ecosystem, characterized — at least under ideal conditions — by extended timelines,2 exceptionally rigorous verification standards and the uncovering of hidden or suppressed information often with moral or legal implications.3 In academic research, investigative journalism is described as “activist, reformer, and exposer”4 that is inherently adversarial, whose goal is to delineate crimes, identify victims and abusers, and advocate for social justice. Unlike routine news, which often recycles available information, investigative reporting begins with a hypothesis or a “denunciation” that must be confirmed or diluted through meticulous research and the collection of evidence, including on-the-ground reporting, confidential-source work and analysis of public records.5

These characteristics highlight why investigative journalism presents a uniquely complex environment for adopting new forms of automation. While AI can augment journalists’ capabilities through large-scale data processing, pattern detection, document handling and information verification, it cannot (as of now) replace the core epistemic and professional functions of investigative reporting. Investigative work often involves standalone cases and thus unique and project-specific methods of investigation: in-person “shoe leather” reporting, “dirty” or “incomplete” data with “messy” formats, hidden or withheld data,6 multisource verification, and high legal and ethical stakes that require human oversight given the need for high accuracy and contextual judgment.7 Current AI systems rely on existing data and patterns and therefore “can neither produce nor verify”8 genuinely new information uncovered through investigative work that is not yet available in digital form. As a result, investigative journalists tend to view AI less as a substitute for human labor and more as a tool that supports time-consuming tasks to allow reporters to focus on uniquely human aspects of the craft, such as source-building, persuasion and accountability reporting.

Altogether, the field of investigative journalism is currently navigating this tension between opportunities and limitations.

From Data and Computational Journalism to AI-Driven Investigations

A central consensus across the literature is that AI systems in investigative journalism should be understood within the broader trajectory of data and computational journalism.

Investigative journalism grounds technological practices like computational journalism within its core watchdog function and normative mission.9 In fact, investigative journalism has historically been at the forefront of adopting computational methods. Early computer-assisted reporting (CAR) in the 1960s used social science methods, spreadsheets and statistical analysis for long-term projects,10 practices that evolved into modern AI-driven techniques, such as machine learning and large-scale data processing.11 As information increasingly shifted to digital formats, investigative journalists turned to computational tools to manage growing volumes of data, exemplified by large-scale projects like the Panama Papers. That project took more than a year, involved millions of documents and required techniques such as optical character recognition (OCR) and algorithmic sorting, as well as large-scale indexing and search capabilities that would otherwise be impossible for human teams to perform.12

These technology-based approaches enabled new pathways to story discovery through pattern recognition, anomaly detection13 and large-scale data linkage,14 while also expanding verification practices through methods like open-source intelligence (OSINT), including satellite analysis and digital forensics.15 Computational journalism in this context applies “computational thinking,” meaning the abstraction of investigative tasks into granular, computable elements that can be processed algorithmically.16

Normative goals — namely accountability, exposure and reform — shape the role of AI systems in investigative journalism, enhancing the watchdog function and efficiency. In this sense, AI tools and data-driven methods are not ends in themselves but tools that support journalism’s public-interest mission by enabling the analysis of large and complex datasets, uncovering hidden patterns and strengthening transparency. 

Use Cases

The range of applications of AI systems in investigative journalism represents targeted interventions across specific stages of the investigative process.

🪡 Finding Needles in Haystacks

Machine learning techniques can reduce massive datasets into actionable leads. For example, Swiss broadcaster SRF trained a supervised classifier on labeled data of authentic and fraudulent Instagram profiles to expose how influencers frequently buy fake engagement.

At the front end, AI tools are particularly valuable for establishing the scale of problems and “finding needles in haystacks.”17 Two studies surveying the uses of AI in investigative journalism18 show that machine learning (ML) is used to reduce massive datasets into actionable leads through data cleaning, record linkage and text-as-data techniques such as sentiment analysis, entity extraction, topic modeling and similarity detection (e.g., Locality-Sensitive Hashing). Another practice-based study with eight investigative journalists in Germany19 showed that journalists see particular value in tools that could automate repetitive web-based research tasks, such as monitoring multiple websites, scraping unstructured local-government records, tracking changes in online content over time, extracting information from social media or leaked materials, and converting collected material into searchable or analyzable formats.

📱 Algorithmic Accountability

Journalists can use AI tools to test opaque systems and platforms at scale. For example, this report found that Spain’s social security agency used opaque AI systems to make high-stakes sick-leave decisions affecting millions of people despite poor accuracy, limited transparency, and little public accountability of the so-called “AI Doctors.”

AI tools play a central role in algorithmic accountability and investigative work in constrained environments. While this briefing does not include a comprehensive look at investigative journalism about AI systems, it does include cases where journalists use AI tools to investigate opaque platform systems. In a practice-based study conducted in a Dutch context20 they describe using AI tools to audit and reverse-engineer opaque platform systems as “fighting fire with fire.” In collaboration with De Groene Amsterdammer, these researchers created sock-puppet accounts and used a vision-language model to simulate user behavior on TikTok, demonstrating that the platform could infer user interests within seconds and begin amplifying eating-disorder content within minutes. In a separate audit of a Google dataset, the same researchers used the ML tool Homepage2vec to classify 2.5 million Dutch-language websites included in large language model training data and uncovered significant proportions of copyrighted journalism, conspiracy content and even leaked personal data. Complementing these efforts, ProPublica’s “Machine Bias” investigation revealed systemic racial disparities in algorithms used in the criminal justice system. 

🗺 Reconstruction and Networking Mapping

In addition to performing algorithmic accountability through audits, AI tools also support modeling and reconstruction in transnational investigations. For example, AI systems have been used to align ship-tracking data with distress signals and survivor testimony to reconstruct migrant deaths in the Mediterranean.21 In another practice-based study — a collaboration of ​​data scientists, AI experts and journalists with the Norwegian Association for Investigative Journalism — one project involved using graph databases to map financial and regulatory networks in Norway’s petroleum sector.22

🛡 Subterfuge and Source Protection

In regions with limited press freedom, the use of generative AI becomes closely tied to practices of subterfuge and source protection. A focus group study of 34 investigative journalists across five East African countries showed that AI tools are enabling journalists to investigate indirectly through data analysis, simulation and anonymization while minimizing exposure.23 For example, Venezuelan news anchors use AI avatars to avoid being identified.

📖 Innovative Storytelling

Finally, AI tools offer new storytelling formats — such as automated text generation, personalization and versioning using Natural Language Generation (NLG) and generative AI — expanding how investigative findings are produced and communicated to diverse audiences.24 For example, Clarín offers a versioning tool that allows readers to access any story in six additional formats: summary, timeline, a comparison of numerical figures, a list of quotations, an index of proper names and an FAQ format.

AI Limits and Constraints in Investigative Journalism

The constraints and limitations of AI systems in investigative journalism fall under five interrelated domains: data availability and quality; professional identity and dependence on external actors; technical and epistemic limitations; linguistic barriers; and economic factors.

📊 Data Availability and Quality

Investigations may rely on data that is not structured or not publicly available, limiting the value of automation.

One of the most significant limitations of AI tools in investigative journalism is the lack of accessible, reliable and structured data, foundational to both AI systems and investigative work. As discussed, investigative journalism often depends on data that are hidden, incomplete, messy or intentionally withheld, which requires journalists to “request, negotiate, scrape, or purchase” datasets before their analysis can even begin.25 Even when data is available, it can be messy (e.g., spread across multiple sources, poorly formatted, inconsistent over time, lacking metadata), further complicating automation.26 These constraints reflect a broader “infrastructural gap,” in which investigative capacity is systematically limited by factors such as proprietary roadblocks and the absence of accessible public databases. Because AI systems rely heavily on consistently structured data, the risk of inaccurate or misleading outputs increases, making full automation not only unreliable but also impossible.27 

🪪 Professional Identity and Dependence on External Actors

Relying on external technology companies, NGOs and universities for AI tools and infrastructure can raise concerns about influence, information security, copyright, privacy and data protection.

AI systems introduce concerns about the erosion of journalistic autonomy and expertise. Journalists across all subfields emphasize that their “irreplaceable human qualities” (e.g., judgment, fieldwork, interviewing, source-building) remain central to their work despite increasing automation of other areas of work.28 At the same time, reliance on external technology companies, nongovernmental organizations and universities for AI tools and infrastructure can create newsroom dependencies and raise concerns about influence, information security, copyright, privacy and data protection.29 

A comparable dynamic already exists in non-AI investigative practices. For example, during the U.S.-Israel war with Iran, news organizations relied heavily on commercial satellite imagery to verify strikes and assess damage in Iran, but access was restricted when Planet Labs limited imagery distribution at the request of the U.S. government, citing operational security concerns. This shift to a managed, case-by-case release system sharply reduced journalists’ ability to independently document events and diminished the transparency previously enabled by open commercial imagery, illustrating that newsroom capabilities depend on the policies and geopolitical constraints of external data providers. 

This increased dependence on external actors is part of a broader structural transformation in journalism. The “platformization” of news — understood as the rise of platforms as the dominant infrastructural and economic model of the social web30 — applies to the entire news ecosystem. As news organizations increasingly depend on third-party platforms for production tools, distribution channels and revenue streams, their autonomy is affected by systems they do not control.31 More broadly, studies stress that “technology in journalism is not neutral,”32 as its effects depend on institutional contexts and power relations, meaning AI tools can reshape — not just support — journalistic practice. 

💻 Technical and Epistemic Limitations

Using new tools responsibly still requires technical expertise that journalists may not have. In some cases, organizational power dynamics may shape adoption in ways that are not fully strategic; in others, there may be an AI or data literacy gap. The opacity of AI tools can also complicate the accountability and traceability of evidence.

Despite the promise of natural-language tool interactions,33 implementing AI tools in investigative journalism requires substantial technical expertise, along with collaboration across disciplines and roles. AI tools are sometimes complex and difficult to integrate into newsroom workflows, especially when they require customization or advanced technical knowledge. In an interview study with 25 national, regional and local Dutch news media organizations,34 the authors identified a significant “language barrier” between investigative journalists and IT professionals that serves as a major impediment to AI adoption. This barrier manifests in several ways. First, there is a lack of shared vocabulary. Some journalists find it difficult to articulate their specific editorial needs to technical experts because they lack a foundational vocabulary for AI techniques and applications. Second, interviewees noted that IT professionals “look through different glasses” and often assume a level of technical knowledge that journalists do not have, making communication difficult. Third, there is a disconnect between the “work sprints” of IT departments, which may plan activities months in advance, and the fast-paced or unpredictable deadlines of investigative news desks. Fourth, IT professionals are often based outside the newsroom, which further limits the opportunity for the spontaneous, interdisciplinary collaboration needed to co-create AI tools. 

To overcome this challenge, research offers a few recommendations. One is to involve “boundary spanners”35 — individuals who operate at the intersection of journalism and technology and can translate between the two groups. These boundary spanners need not be additional staff; existing personnel can take on these roles and commit to approaching technology from this perspective by bringing together colleagues with distinct expertise. Another recommendation is to take a collaborative approach requiring active participation from all stakeholders, including, at times, those outside journalism.36 

In addition to communication barriers between journalists and IT professionals, AI adoption is shaped by other organizational and professional dynamics. Sometimes AI uptake is driven by enthusiasm for new technologies without strategy, which some journalists and scholars call “Shiny Things Syndrome.”37 As shown in an interview-based study in the Dutch news media context,38 AI discussions sometimes remain at the managerial level and do not translate to everyday newsroom workflows. As a result, many investigative journalists position themselves as “reluctant adopters,” particularly when faced with technical language barriers and the need to collaborate with specialized teams such as data scientists or IT professionals. These professional dynamics are not specific to AI adoption; across a range of methods, earlier research on technology adoption in journalism also shows widespread curiosity about new technologies alongside resistance to top-down technology mandates and concerns about dependency on third-party companies.39

These challenges are closely tied to the broader issue of insufficient AI and data literacy in journalism. Journalists struggle with limited access to reliable learning resources, difficulty identifying appropriate expertise, general reluctance and the challenge of keeping pace with rapidly evolving technologies.40 In response, scholars have proposed frameworks such as the Accuracy-Fairness-Transparency (AFT) framework, which emerged from a synthesis of prior research on AI systems in journalism. AFT applies core journalistic ethics (accuracy, fairness and transparency) to ensure high-quality, trustworthy data in AI-driven journalism. AFT can improve data practices and serve as a pathway for building AI literacy and embedding journalistic values into technological workflows.41 

Additionally, the opacity of AI tools limits journalists’ understanding of their outputs and how and why they are processed.42 This, in turn, complicates accountability and the epistemic traceability of evidence, especially in the context of generative AI.43 Scholars distinguish between explainability — the ability to explain what a model does and what it produces — and interpretability, which refers to understanding the specific mechanics of how the model reached that result.44 Because of these concerns, many investigative teams purposefully opt for “decision tree” or “random forest” algorithms over more powerful neural network or deep learning approaches because they prefer tools that produce human-readable partitions of data that allow for better scrutiny.45 In the specific context of generative AI, the traceability of evidence is further undermined by hallucinations, where models predict plausible but factually incorrect data points. This lack of reliability has led some newsrooms to abandon high-performing AI anomaly detection tools.46

Finally, many tools remain project-specific, limiting their reuse and reducing long-term efficiency gains. These constraints mean that AI adoption is not simply a matter of availability but of sustained organizational capacity, training and collaboration.47

🗣 Linguistic Barriers

Natural-language technologies continue to perform best in English and in Euro-American cultural contexts. These dynamics may reinforce professional hierarchies globally rather than leveling them.

AI systems in investigative journalism create another type of language barrier, particularly in non-English and Global South contexts, because the underlying technologies are primarily developed, trained and optimized for English. This results in clear technical performance gaps: while English-based models are highly-developed, even widely used languages like French remain comparatively underdeveloped and “clumsy,”48 and AI tools often fail to function effectively in many non-Anglophone languages.49 As a result, journalists (including non-investigative journalists) in these contexts face limitations in using AI tools for core tasks like data mining, transcription and automated analysis.50 These constraints are compounded by a broader “Eurocentric” information lens, where AI systems — shaped by Western design and data — struggle to interpret local cultural nuances and are viewed by some as tools that extract information for Western purposes rather than supporting local knowledge production. Because they are not trained on localized linguistic and cultural data, such systems remain fundamentally exclusionary in their ability to represent non-Western perspectives.51

These dynamics extend into professional and educational domains, where English-centric AI systems reinforce existing hierarchies and forms of exclusion. An interview and focus group study of 34 investigative journalists across five East African countries shows that in some newsrooms, competence is tied to English proficiency rather than investigative skill, leading to the loss of talented journalists who are unable to communicate their work effectively in English. AI tools can intensify this divide by amplifying the capabilities of those already fluent in dominant languages and technologies, while further marginalizing those without these skills.52 A related mixed-methods study of journalism education across four South African universities found similar resistance to AI tools that lack support for Indigenous language contexts, prompting calls to “decolonize” language models by training them on local languages and contexts. Without such efforts, investigative AI tools risk continuing to privilege English-speaking environments and limiting locally grounded journalism.53 For example, Latam-GPT, developed by Chile’s National Center for Artificial Intelligence (CENIA), aims to build an open-source AI model trained on Latin American languages and contexts (see this working group’s November 2025 briefing on translation and transcription for further discussion of linguistic strengths and limitations). 

💸 Economic Factors

Investigations are highly individual, which limits productivity gains and economies of scale.

From an economic perspective, investigative journalism faces some disadvantages that limit the broader use of AI. For one, investigative reporting relies heavily on human effort. Unlike advertising or other automated communication fields, it is much harder to use technology to speed up the deep, manual work required for investigations. Furthermore, while the social importance of investigative journalism remains constant, its production costs increase relative to other sectors, often branding it as “costly” by comparison.54 This creates a condition in which AI tools accelerate and facilitate some tasks, such as data processing, but fail to reduce the overall labor. Moreover, the limited productivity gains from AI systems in investigative journalism, relative to the significant gains in sectors like advertising and public relations, create a cost disadvantage.55 This “cost disease” makes it economically difficult for newsrooms to fund competitive wages for highly skilled human labor, increasing the risk of talent drain as journalists move to better-paying communication sectors with overlapping skill sets.56 

As discussed earlier, these constraints are compounded by the project-specific nature of AI systems in investigative journalism.57 As a result, AI adoption tends to be viable only for a narrow subset of investigations where data is central, automation is technically feasible and simpler computational methods or traditional reporting cannot achieve comparable results. These financial barriers are even more pronounced for smaller and local newsrooms, which are less able to absorb the high costs of AI development or even to customize off-the-shelf tools — sometimes paired with infrastructural issues, such as unreliable internet connectivity58 — and therefore face greater barriers to adoption, reinforcing existing inequalities in the distribution of AI capabilities across the industry.59

Global Perspectives

Working group member Gregory Gondwe writes:

“Research from Africa and other parts of the Global South suggests that the adoption of AI in investigative journalism is influenced by the practical realities of reporting within resource-constrained environments, uneven digital infrastructures, and varying levels of press freedom. A 2025 Thomson Reuters Foundation survey of journalists across more than 70 countries found widespread use of AI tools but limited institutional governance, with many newsrooms lacking formal policies or training structures.60 Across these contexts, journalists adapt AI systems to address localized challenges associated with incomplete datasets, language barriers, weak information infrastructures, and political constraints.

“One example comes from East Africa, where investigative journalists use AI-assisted tools to support both reporting and personal security. In a study of journalists from the Democratic Republic of Congo, Ethiopia, Rwanda, Tanzania, and Uganda, participants described using AI-supported anonymization, encrypted communications, automated monitoring tools, and other forms of digital subterfuge while investigating corruption, human rights abuses, and state misconduct.61 Journalists viewed these technologies as tools that help sustain investigative work under conditions of surveillance and political pressure. The study also found that technological adaptation occurs on both sides of the information environment, with journalists deploying protective tools while governments expand their own AI-enabled surveillance capabilities.

“A second example highlights the role of computational tools in addressing weaknesses within public information infrastructures. Journalists investigating procurement corruption across several African countries have combined fuzzy matching, clustering techniques, and extensive manual verification to connect fragmented government records with leaked documents and supplier databases. These investigations demonstrate how successful AI-supported reporting depends on the interaction between computational analysis and human judgment. Patterns discovered through automated techniques still require contextual interpretation, source development, and rigorous verification before they become publishable evidence.62“

Where More Research Would Be Helpful

  • Cross-industry comparison: More research is needed to compare how AI tools are adopted across journalistic subfields to better understand their unique applications and limitations in investigative contexts.
  • Generalizable global research with investigative reporters: Much of the literature analyzed in this briefing used practice-based research techniques, bringing together investigative journalists, academics, data journalists and data scientists, to achieve a deeper understanding of investigative journalism as it is practiced today. While these provide deep insights, there is a clear opportunity for future studies to adopt broader quantitative approaches, such as large-scale surveys, to capture a more comprehensive global outlook on how investigative journalists use and perceive AI systems in their professional practice.
  • Global South and context-sensitive AI: Most existing research on AI applications for investigative journalism focuses on the United States and Europe. Future work should focus more on developing and evaluating AI tools that are tailored to non-Western, non-English and culturally specific contexts in investigative journalism. 
  • Emerging and evolving nature of AI: Given the rapid evolution of AI technologies and the limited number of existing studies, ongoing empirical research is needed to track their long-term impacts on investigative journalism.
  • Local newsrooms: More research should examine how under-resourced newsrooms adopt, adapt to or are excluded from AI-driven investigative practices.
  • Sustainable funding models: Future studies should explore viable and independent funding models that support AI innovation in investigative journalism without increasing reliance on external actors or compromising editorial autonomy.

Notable Cases

The following cases highlight that, across all regions, the consensus remains that AI tools serve as a force multiplier rather than a replacement for human labor. They enable journalists to examine data creatively and pursue stories that would otherwise be buried under the sheer scale of modern information.

Data sifting and lead generation

  • AP Local News AI: In collaboration with AppliedXL, the Associated Press developed AP Local News AI. This tool monitors federal agency data to identify how national regulations impact specific local communities, effectively “anticipating” stories before they break.
  • Datashare: It is a free, open-source desktop software developed by the International Consortium of Investigative Journalists (ICIJ) that enables users to index, search and analyze large volumes of documents (e.g., PDFs, emails, images) locally or on a server. 
  • Nubia AI: an AI-powered data journalism platform designed to transform complex, messy datasets, such as PDFs, spreadsheets and satellite imagery, into structured data and publishable, newsroom-grade stories. Developed by Dataphyte Nigeria in 2022, it is designed to help journalists, researchers and civic organizations produce data-driven stories quickly and accurately.

Fact-checking and verification

  • StatCheck: Developed by Inria (National Institute for Research in Digital Science and Technology) and RadioFrance, StatCheck automates the verification of statistical claims by cross-referencing articles against the INSEE and Eurostat databases.
  • Dubawa: To combat regional misinformation in Western Africa, the Dubawa project launched an AI-powered chatbot and audio platform specifically designed to debunk viral falsehoods.

Content production and contextualization

  • Odin: The Colombian outlet Cuestión Pública developed Odin, an AI system that connects past investigations to breaking news. Using retrieval-augmented generation (RAG), it searches proprietary databases and generates draft content grounded in verified reporting. The tool combines BERT for data indexing with GPT-4 for drafting, reducing production time from hours to minutes while maintaining editorial control.

Resources and Practical Guidelines 

While academic research on AI in investigative journalism remains relatively limited, a growing body of practitioner-oriented resources offers guidance on how these tools are being used in newsrooms. These sources are particularly valuable because they translate technical possibilities into replicable workflows, editorial strategies and ethical considerations. Key examples include:

Scope of included works

The reviewed studies span an international landscape, covering more than 20 countries and regions across Europe, North America, Africa and Asia and several cross-regional contexts such as Eastern Africa, Southeast Asia and Western Europe. Methodologically, the literature reflects a strong dominance of qualitative and mixed-methods approaches. Several studies also incorporated literature reviews, prototype development, usability testing and data-driven analytical techniques such as topic modeling, demonstrating substantial methodological diversity within the field.

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

Claudia Báez 
Associate Consultant, Fathm

Jay Barchas-Lichtenstein
Senior Research Manager, Center for News, Technology & Innovation

Madhav Chinnappa
Independent Media Consultant

Utsav Gandhi
PhD Student, University of Illinois Chicago

Gregory Gondwe
Assistant Professor of Journalism and Emerging Media Technologies, California State University, San Bernardino

K.V. Kurmanath
Senior Journalist and Academic

Amy Mitchell
Executive Director, Center for News, Technology & Innovation

Chris Moran 
Head of Editorial Innovation, Guardian News & Media

Sophie Morosoli
Postdoctoral Researcher at the AI, Media & Democracy Lab, University of Amsterdam

Afrooz Mosallaei
Research Associate, Center for News, Technology & Innovation

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

Oluseyi Olufemi
Nigeria Country Director, Dataphyte

Esteban Ponce de León
Resident Fellow, Digital Forensic Research Lab (DFRLab) at the Atlantic Council

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

Sabina Tomkins
Assistant Professor of Information, School of Information and Faculty Associate, Center for Political Studies, Institute for Social Research, University of Michigan

Jaemark Tordecilla
Independent Media Advisor, Philippines

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Diakopoulos, N. (2024). Data journalism: The emergence of computational journalism at Georgia Tech, 2006–2008. In J. Pavlik (Ed.) Milestones in Digital Journalism (1st ed., pp. 1-16). Routledge. 

Dierickx, L., Opdahl, A. L., Khan, S. A., Lindén, C.-G., & Guerrero Rojas, D. C. (2024). A data-centric approach for ethical and trustworthy AI in journalism. Ethics and Information Technology, 26(4), 64. https://doi.org/10.1007/s10676-024-09801-6 

Fridman, M., Krøvel, R., & Palumbo, F. (2025). How (not to) run an AI project in investigative journalism. Journalism Practice, 19(6), 1362–1379. https://doi.org/10.1080/17512786.2023.2253797 

Ganguly, M. (2022). The future of investigative journalism in the age of automation, open-source intelligence (OSINT) and artificial intelligence (AI). PhD thesis, University of Westminster, Westminster School of Media and Communication. https://doi.org/10.34737/vx128 

Gondwe, G. (2025). Investigative journalism and AI-driven subterfuge in countries with limited press freedom in East Africa. Journalism Practice, 0(0), 1–20. https://doi.org/10.1080/17512786.2025.2541254 

Grimme, M. (2021). Factors Influencing the Rejection of Automated Journalism: A Systematic Literature Review. Nordic Journal of Media Management, 2(1), 3–21. https://doi.org/10.5278/njmm.2597-0445.6826

Helmond, A. (2015). The platfomization of the web: Making web data platform ready. Social media & Society, 1(2).

Hofeditz, L., Jung, A.-K., Mirbabaie, M., & Stieglitz, S. (2025). Ethical guidelines for the application of generative AI in German journalism. Digital Society, 4(1), 4. https://doi.org/10.1007/s44206-024-00151-w 

Hollanek, T., Peters, D., Drage, E., & Hernandes, R. (2025). AI, journalism, and critical AI literacy: Exploring journalists’ perspectives on AI and responsible reporting. AI & SOCIETY, 40(8), 6393–6405. https://doi.org/10.1007/s00146-025-02407-6 

Kasica, S., Berret, C., & Munzner, T. (2023). Dirty data in the newsroom: Comparing data preparation in journalism and data Science. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.1145/3544548.3581271 

Katches, M. (2014, November 30). 33 tips to keep daunting investigative reporting projects on track. Reveal. https://revealnews.org/article/33-tips-to-keep-daunting-investigative-reporting-projects-on-track/ 

Küng, L. (2013). Innovation, technology and organisational change. In T. Storsul & A. H. Krumsvik (Eds.), Media innovations: A multidisciplinary study of change (pp. 9-12). Nordicom.

Lischka, J. A., Mollen, A., Kristensen, L. M., Kunert, J., Braun, A. F., Koitie, P. K., Rolandsson, T., Schaetz, N., & Kammer, A. (2025). What is data? A conceptual and empirical inquiry of the facets of data in news organizations. SocArXiv. https://doi.org/10.31235/osf.io/2t6br_v2 

Manolescu, M., & Vaudano, I. (2024, November 6). AI and the media: A (r)evolution in investigative reporting? Polytechnique insights. https://www.polytechnique-insights.com/en/columns/science/ai-and-the-media-a-revolution-in-investigative-reporting/ 

Mari, W. (2024). The Pre-History of News-Industry Discourse Around Artificial Intelligence. Emerging Media, 2(3), 499–522. https://doi.org/10.1177/27523543241279577 

Mohamedy, H., & Wandwi, G. (2025). How digital tools are transforming investigative journalism in practice. Journalism Practice, 0(0), 1–27. https://doi.org/10.1080/17512786.2025.2605212 

Møller, L. A., van Dalen, A., & Skovsgaard, M. (2025). A little of that human touch: How regular journalists redefine their expertise in the face of artificial intelligence. Journalism Studies, 26(1), 84–100. https://doi.org/10.1080/1461670X.2024.2412212 

Ncube, L., Mofokeng, R. W., Chibuwe, A., Munoriyarwa, A., & Murangi, A. K (2025). ‘Mind the gap’: Artificial intelligence and journalism training in Southern African journalism schools. Media Practice and Education, 0(0), 1–17. https://doi.org/10.1080/25741136.2025.2464483 

Örnebring, H. (2010). Technology and journalism-as-labour: Historical perspectives. Journalism, 11(1), 57–74. https://doi.org/10.1177/1464884909350644

Poell, T., Nieborg, D. B., & Duffy, B. E. (2023). Spaces of negotiation: Analyzing platform power in the news industry. Digital Journalism, 11(8), 1391–1409. https://doi.org/10.1080/21670811.2022.2103011 

Posetti, J. (2018). Time to step away from the ‘bright, shiny things’? Towards a sustainable model of journalism innovation in an era of perpetual change. Reuters Institute.

Radcliffe, D. (2025). Journalism in the AI era: Opportunities and challenges in the Global South and emerging economies. Thomson Reuters Foundation. https://www.trust.org/resource/ai-revolution-journalists-global-south/ 

Simon, F. M. (2022). Uneasy bedfellows: AI in the news, platform companies and the issue of journalistic autonomy. Digital Journalism, 10(10), 1832–1854. https://doi.org/10.1080/21670811.2022.2063150 

Stray, J. (2019). Making artificial intelligence work for investigative journalism. Digital Journalism, 7(8), 1076–1097. https://doi.org/10.1080/21670811.2019.1630289 

Thurman, N., Lewis, S. C., & Kunert, J. (2019). Algorithms, automation, and news. Digital Journalism, 7(8), 980992. https://doi.org/10.1080/21670811.2019.1685395 

Thurman, N., Thäsler-Kordonouri, S., & Fletcher, R. (2025). AI adoption by UK journalists and their newsrooms: surveying applications, approaches, and attitudes. https://doi.org/10.60625/risj-ea11-q402  

Umeora, C. C. (2025). The impact of AI on investigative journalism: Opportunities and challenges for Nigerian media professionals. International Journal of Innovative Information Systems and Technology Research, 13(1), 108-117.

Veerbeek, J. (2025). Fighting fire with fire: Journalistic investigations of artificial intelligence using artificial intelligence techniques. Journalism Practice, 1–19. https://doi.org/10.1080/17512786.2025.2479499

Wellbrock, C. M. (2024). Generative AI and investigative journalism—An application of Baumol’s Cost Disease across mass communication sectors. Emerging Media, 2(4), 663–672. https://doi.org/10.1177/27523543241299631

Footnotes

  1. Mari, 2024 ↩︎
  2. Broussard, 2015; Chijioke, 2021; Čakš et al., 2025; da Silva, 2023; Ganguly, 2022; Katches, 2014 ↩︎
  3. Boukhssas, 2026; da Silva, 2023 ↩︎
  4. De Cooker et al., 2025 ↩︎
  5. da Silva, 2023 ↩︎
  6. Wellbrock, 2024 ↩︎
  7. Fridman et al., 2025 ↩︎
  8. Wellbrock, 2024 ↩︎
  9. da Silva, 2023 ↩︎
  10. Coddington, 2015 ↩︎
  11. Lischka et al., 2025 ↩︎
  12. Diakopoulos, 2024 ↩︎
  13. Diakopoulos, 2024 ↩︎
  14. Coddington, 2015 ↩︎
  15. da Silva, 2023 ↩︎
  16. Diakopoulos, 2024 ↩︎
  17. Bradshaw, 2025; Cifliku & Heuer, 2025 ↩︎
  18. Bradshaw, 2025; Stray, 2019 ↩︎
  19. Cifliku & Heuer, 2025 ↩︎
  20. Veerbeek, 2025 ↩︎
  21. Mohamedy & Wandwi, 2025 ↩︎
  22. Fridman et al., 2023 ↩︎
  23. Gondwe, 2025 ↩︎
  24. Bradshaw, 2025 ↩︎
  25. Wellbrock, 2024 ↩︎
  26. Kasica et al., 2023; Cifliku & Heuer, 2025 ↩︎
  27. Wellbrock, 2024 ↩︎
  28. Møller et al., 2025 ↩︎
  29. Bradshaw, 2025 ↩︎
  30. Helmond, 2015 ↩︎
  31. Poell et al., 2022; Simon, 2022 ↩︎
  32. Mohamedy & Wandwi, 2025 ↩︎
  33. Dalgali & Crowston, 2020 ↩︎
  34. De Cooker et al., 2025 ↩︎
  35. De Cooker et al., 2025 ↩︎
  36. Fridman et al., 2025; Mohamedy & Wandwi, 2025 ↩︎
  37. Posetti 2018; see also Broussard et al. 2019; Kueng 2017; Thurman et al., 2019 ↩︎
  38. De Cooker et al., 2025 ↩︎
  39. For example, see Örnebring, 2010; Grimme, 2021. See also Møller et al. 2025 for AI specifically. ↩︎
  40. De Cooker et al., 2025; Hollanek et al., 2025; Thurman et al., 2025 ↩︎
  41. Dierickx et al., 2024 ↩︎
  42. Bradshaw, 2025; Mohamedy & Wandwi, 2025 ↩︎
  43. Hofeditz et al., 2025 ↩︎
  44. Becker, 2023; de-Lima-Santos et al., 2024 ↩︎
  45. Bradshaw, 2025 ↩︎
  46. Bradshaw, 2025 ↩︎
  47. Stray, 2019 ↩︎
  48. Manolescu & Vaudano, 2024 ↩︎
  49. CNTI, 2025 ↩︎
  50. Ncube et al., 2025 ↩︎
  51. Ncube et al., 2025 ↩︎
  52. Gondwe, 2025 ↩︎
  53. Ncube et al., 2025 ↩︎
  54. Wellbrock, 2024 ↩︎
  55. Wellbrock, 2024 ↩︎
  56. Stray, 2019 ↩︎
  57. Stray, 2019; Veerbeek, 2025 ↩︎
  58. Umeora, 2025 ↩︎
  59. de-Lima-Santos & Ceron, 2022 ↩︎
  60. Radcliffe, 2025 ↩︎
  61. Gondwe, 2025 ↩︎
  62. Chimoio, 2026 ↩︎

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]]> AI for Human Propaganda https://cnti.org/news-clip/ai-for-human-propaganda/ Wed, 01 Apr 2026 18:15:00 +0000 https://cnti.org/?post_type=news-clip&p=11417 While journalism cultures around the world grapple seriously with the impact of AI, China’s closed and repressive media system can only celebrate the trend as a technological boost for the storytelling of the state.

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]]> While journalism cultures around the world grapple seriously with the impact of AI, China’s closed and repressive media system can only celebrate the trend as a technological boost for the storytelling of the state.

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]]> Takeaways from the India AI Impact Summit https://cnti.org/event-outtakes/takeaways-from-the-india-ai-impact-summit/ Fri, 06 Mar 2026 19:13:02 +0000 https://cnti.org/cnti-news// The scale of the India AI Summit has expanded significantly from the early global AI gatherings just a few years ago.

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]]> I recently attended the @IndiaAI Summit in New Delhi on behalf of @CNTI. Over five days, the summit reflected a clear sense of ambition. The scale has expanded significantly from the early global AI gatherings just a few years ago. At Bharat Mandapam, the main venue of the summit, themes such as “sovereignty,” “middle power,” and “democratization” reverberated through crowded halls. Startups and major technology companies showcased new deployments in maze-like pavilions, and high-profile commitments were unveiled in carefully staged ceremonies. The political will behind India’s AI push was evident, and it was encouraging to see key policymakers, researchers, industry leaders, and users networking together in the same space. 

The inclusion of students and young professionals in large numbers was heartening as many are already habitual users of AI tools and attended less as spectators and more as participants trying to understand where the technology is headed — and how to position themselves alongside it. Their presence underscored how quickly AI has shifted from a specialist policy conversation to an everyday technology shaping learning, work, and access to information.

One of the more substantive discussions focused on AI and journalism, where leaders from major Indian news organizations examined the structural pressures generative AI is placing on the news economy. Speakers highlighted the rise of AI-generated summaries and “zero-click” search experiences that reduce referral traffic to publisher websites, raising questions about how original reporting will continue to inform people and be funded. Editors emphasized that journalism depends on sustained investment in reporting and editorial oversight, even as AI systems increasingly rely on high-quality news content in their training and outputs. Panelists argued that AI systems participating in the distribution and summarization of news should meet higher standards of accountability and fair compensation. At the same time, they recognized AI’s potential to strengthen archives, improve workflows, and support subscription growth.

All in all, the @IndiaAI Summit was an important forum to move the conversation on how AI will be integrated into journalism and the broader economy — and what governance frameworks that integration will require.

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]]> AI Versus Accuracy? We’re Willing to Make the Trade-Off https://cnti.org/news-clip/ai-versus-accuracy-were-willing-to-make-the-trade-off/ Thu, 26 Feb 2026 15:47:59 +0000 https://cnti.org/?post_type=news-clip&p=10600 When asking AI about the news, readers “know the answers they are getting are not perfect.” They keep asking anyway.

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]]> When asking AI about the news, readers “know the answers they are getting are not perfect.” They keep asking anyway.

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]]> AI Basic Training: Newsrooms Offer Little Practical Guidance https://cnti.org/news-clip/commentary/ Mon, 23 Feb 2026 19:24:16 +0000 https://cnti.org/?post_type=news-clip&p=11421 The post AI Basic Training: Newsrooms Offer Little Practical Guidance appeared first on Center for News, Technology & Innovation.

]]>

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]]> Newsroom Policies for AI in Journalism https://cnti.org/reports/newsroom-policies-for-ai-in-journalism-2/ Tue, 17 Feb 2026 15:25:55 +0000 https://cnti.org/cnti-news// The third briefing from the AI and Journalism Research Working Group finds that organizational AI policies tend to prioritize principles and values over practical guidance.

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]]> Introduction

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.

About

This is the third 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 18 cross-industry members from around the world, bringing research, journalism and technology expertise to the discussions. 

The goal of the working group is to offer succinct summaries of global research in specific topics at the intersection of journalism and AI. Each quarter, the working group synthesizes the state of research across two to three topics for journalism practitioners, researchers and industry leaders around the world, focusing on actionable recommendations for journalism — not other fields that are concerned with AI.

In each report, we lay out the general findings of the research to date, considerations and/or actions for practitioners and areas where more or new research is needed. 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 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,3 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.

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.

Findings
  • Neither journalism nor journalistic values stay exactly the same over time; technological changes have always raised new questions.
  • Newsroom policies on new technologies tend to emphasize principles and values but do not often offer practical guidance. It would be valuable for policies to include more detail on algorithms and systems in addition to outputs, and to lay out considerations for working with third-party tools.
    • In particular, guidelines for procurement are rarely addressed, even though the tools’ underlying algorithms may subtly influence media organizations’ editorial decisions.5 
  • When developing guidelines for the use of new technologies, it is essential to include people with different personal and professional backgrounds to ensure guidelines address a broad range of use cases and impacts.

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)

Global perspectives

Working group member Claudia Báez shares her perspective:

“In my experience working with AI in Latin American newsrooms, there is a clear gap between having an AI policy ‘on paper’ and making it widely accessible for everyone or democratizing it. While large legacy and digital organizations often create formal frameworks or transparency statements, these documents are rarely integrated into the newsroom’s daily workflow. As a result, journalists use AI frequently without oversight. They work with the organization’s information using personal AI tools, sometimes free versions that offer no meaningful data protection and could accidentally make sensitive company information public. Then the ‘human-in-the-loop’ is retained as a concept rather than as a practical safety measure. 

“The recent crisis at El Espectador in Colombia, where AI-generated misinformation went unnoticed for months, underscores the risks of this oversight gap. These examples speak to the importance of ensuring that policy development does not only live on paper but includes active connections to and evaluation of practices. One promising example comes from La Silla Rota, a Mexican legacy media organization, which has created an internal AI policy tool for its team. This simple custom GPT is shared with the newsroom to answer journalists’ questions about when to use AI and when not to. practitioners, AI technologists and development policy actors.”

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.

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

Claudia Báez 
Associate Consultant, Fathm

Jay Barchas-Lichtenstein
Senior Research Manager, Center for News, Technology & Innovation

Madhav Chinnappa
Independent Media Consultant

Utsav Gandhi
PhD Student, University of Illinois Chicago

K.V. Kurmanath
Senior Journalist and Academic

Amy Mitchell
Executive Director, Center for News, Technology & Innovation

Chris Moran 
Head of Editorial Innovation, Guardian News & Media

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

Oluseyi Olufemi
Nigeria Country Director, Dataphyte

Esteban Ponce de León
Resident Fellow, Digital Forensic Research Lab (DFRLab) at the Atlantic Council

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

References

Becker, K. B., Simon, F. M., & Crum, C. (2025). Policies in Parallel? A Comparative Study of Journalistic AI Policies in 52 Global News Organisations. Digital Journalism, 13(9), 1578-1598. https://doi.org/10.1080/21670811.2024.2431519

Cools, H., & Diakopoulos, N. (2023, July 10). Towards Guidelines for Guidelines on the Use of Generative AI in Newsrooms. Generative AI in the Newsroom. https://generative-ai-newsroom.com/towards-guidelines-for-guidelines-on-the-use-of-generative-ai-in-newsrooms-55b0c2c1d960

de-Lima-Santos, M.-F., Yeung, W. N., & Dodds, T. (2024). Guiding the way: A comprehensive examination of AI guidelines in global media. AI & SOCIETY. https://doi.org/10.1007/s00146-024-01973-5

Dodds, T., Vandendaele, A., Simon, F. M., Helberger, N., Resendez, V., & Yeung, W. N. (2025). Knowledge Silos as a Barrier to Responsible AI Practices in Journalism? Exploratory Evidence from Four Dutch News Organisations. Journalism Studies, 26(6), 740–758. https://doi.org/10.1080/1461670X.2025.2463589

Duffy, A., & Knight, M. (2019). Don’t be Stupid: The role of social media policies in journalistic boundary-setting. Journalism Studies, 20(7), 932–951. https://doi.org/10.1080/1461670X.2018.1467782

Goodson, K., Barchas-Lichtenstein, J., Jens, S., Wright, E., & Gandhi, U. (2025). Journalism’s New Frontier: An Analysis of Global AI Policy Proposals and Their Impacts on Journalism. Center for News, Technology & Innovation. https://cnti.org/reports/journalisms-new-frontier-an-analysis-of-global-ai-policy-proposals-and-their-impacts-on-journalism/

Harlow, S. (2023). Protecting News Companies and Their Readers: Exploring Social Media Policies in Latin American Newsrooms. In Digital Journalism in Latin America. Routledge.

Herrera-Damas, S. (2014). Recurring topics in the social media policies of mainstream media. Journal of Applied Journalism & Media Studies, 3(2), 155–173. https://doi.org/10.1386/ajms.3.2.155_1

Hofeditz, L., Jung, A.-K., Mirbabaie, M., & Stieglitz, S. (2025). Ethical Guidelines for the Application of Generative AI in German Journalism. Digital Society, 4(1), 4. https://doi.org/10.1007/s44206-024-00151-w

Ifayemi, S., Tabassi, E., & Deckard, A. C. (2025, July 28). Decoding AI Governance: A Toolkit for Navigating Evolving Norms, Standards, and Rules. Partnership on AI. https://partnershiponai.org/resource/decoding-ai-governance/

Kalfeli, P., & Angeli, C. (2025). The Intersection of AI, Ethics, and Journalism: Greek Journalists’ and Academics’ Perspectives. Societies, 15(2). https://doi.org/10.3390/soc15020022

Lefèvre, B., Errando, A., Afilipoaie, A., Ranaivoson, H., & Wiart, L. (2025). Exploring ethical and regulatory challenges of AI integration in European Union Newsrooms. Media Studies, 16(31), 31–55.

Lu, S., Wei, L., & Liang, H. (2025). Social Media Policies as Social Control in the Newsroom: A Case Study of the New York Times on Twitter. Journalism Studies, 26(5), 568–586. https://doi.org/10.1080/1461670X.2025.2452265

Mari, W. (2024). The Pre-History of News-Industry Discourse Around Artificial Intelligence. Emerging Media, 2(3), 499–522. https://doi.org/10.1177/27523543241279577

Mbaabu, N. M. (2025). Examining the status of AI use guidelines in editorial policies of Kenyan digital media houses and challenges in their formulation and implementation in newsrooms [Master of Arts in Digital Journalism]. Aga Khan University.

Miller, K. C., & Nelson, J. L. (2022). “Dark Participation” Without Representation: A Structural Approach to Journalism’s Social Media Crisis. Social Media + Society, 8(4), 20563051221129156. https://doi.org/10.1177/20563051221129156

Molyneux, L., & Nelson, J. L. (2024). “Let’s Not Tank the Reputation of This Organization.” How Newsroom Social Media Policies Exacerbate Journalism’s Labor Crisis. Journalism Studies, 25(9), 931–950. https://doi.org/10.1080/1461670X.2023.2263797

Peck, G. A. (2023). The First Draft of AI Policy. Editor & Publisher, 156(9), 32–34.

Piasecki, S., & Helberger, N. (2025). A nightmare to control: Legal and organizational challenges around the procurement of journalistic AI from external technology providers. The Information Society, 41(3), 173–194. https://doi.org/10.1080/01972243.2025.2473398

Porlezza, C. (2024). The datafication of digital journalism: A history of everlasting challenges between ethical issues and regulation. Journalism, 25(5), 1167–1185. https://doi.org/10.1177/14648849231190232

Radcliffe, D. (2025). Journalism in the AI Era: Opportunities and Challenges in the Global South. https://doi.org/10.13140/RG.2.2.16814.63043

Sacco, V., & Bossio, D. (2017). Don’t Tweet This!: How journalists and media organizations negotiate tensions emerging from the implementation of social media policy in newsrooms. Digital Journalism, 5(2), 177–193. https://doi.org/10.1080/21670811.2016.1155967

Sánchez-García, P., Diez-Gracia, A., Mayorga, I. R., & Jerónimo, P. (2025). Media Self-Regulation in the Use of AI: Limitation of Multimodal Generative Content and Ethical Commitments to Transparency and Verification. Journalism and Media, 6(1), Article 1. https://doi.org/10.3390/journalmedia6010029

Seipp, T. J., Helberger, N., De Vreese, C., & Ausloos, J. (2024). Between the cracks: Blind spots in regulating media concentration and platform dependence in the EU. Internet Policy Review, 13(4). https://doi.org/10.14763/2024.4.1813

Simon, F. M. (2022). Uneasy Bedfellows: AI in the News, Platform Companies and the Issue of Journalistic Autonomy. Digital Journalism, 10(10), 1832–1854. https://doi.org/10.1080/21670811.2022.2063150

Simon, F. M. (2024). Escape Me If You Can: How AI Reshapes News Organisations’ Dependency on Platform Companies. Digital Journalism, 12(2), 149–170. https://doi.org/10.1080/21670811.2023.2287464

Simon, F. M. (2025). Rationalisation of the news: How AI reshapes and retools the gatekeeping processes of news organisations in the United Kingdom, United States and Germany. New Media & Society, 14614448251336423. https://doi.org/10.1177/14614448251336423

Solaroli, M. (2017). News Photography and the Digital (R)evolution: Continuity and Change in the Practices, Styles, Norms and Values of Photojournalism. In J. Tong & S.-H. Lo (Eds.), Digital Technology and Journalism: An International Comparative Perspective (pp. 47–70). Springer International Publishing. https://doi.org/10.1007/978-3-319-55026-8_3

Timcke, S., & Schroeder, Z. (2025, July 10). M20 Policy Brief 4: Power, Politics, and Economics – AI, Africa and the G20. Media20. https://media20.org/2025/07/10/m20-policy-brief-4-power-politics-and-economics-ai-africa-and-the-g20/

Tseng, E., Young, M., Le Quéré, M. A., Rinehart, A., & Suresh, H. (2025). “Ownership, Not Just Happy Talk”: Co-Designing a Participatory Large Language Model for Journalism. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 3119–3130. https://doi.org/10.1145/3715275.3732198

Umejei, E., Ayisi, A., Phiri, M., & Tallam, E. (2025). Artificial Intelligence and Journalism in Four African Countries: Optimists, Pessimists, and Pragmatists. Journalism Practice, 19(10), 2249–2265.https://doi.org/10.1080/17512786.2025.2489590

van Drunen, M. Z. (2025). Safeguarding media freedom from infrastructural reliance on AI companies: The role of EU law. Telecommunications Policy, 102990.https://doi.org/10.1016/j.telpol.2025.102990

Appendix

Papers referenced in this briefing

PaperScope & Methods
Becker et al., 2025AI policies: 52 news organizations in 12 countries
Cools & Diakopoulos, 2023AI policies: 21 guidelines
de-Lima-Santos et al.AI policies: 37 guidelines (from news organizations & coalitions) in 17 countries
Dodds et al., 2025Responsible AI: 14 semi-structured interviews with Dutch editors, managers and journalists
Duffy & Knight, 2019Social media policies: 17 news organizations in 4 countries
Goodson et al., 2025AI policies: 188 legal frameworks, laws and bills
Harlow, 2023Social media policies: survey of 1,094 Latin American journalists
Herrera-Damas, 2014Social media policies: 22 newsrooms
Hofeditz et al. 2025AI policies: 18 in-depth interviews with German AI and journalism experts and a review of existing guidelines
Kalfeli & Angeli, 2025AI policies: 28 semi-structured interviews with Greek journalists and academics
Lefèvre et al., 2025Political economy analysis of the use of AI tools in newsrooms: 30 key documents and 41 interviews with media professionals and regulatory experts in Belgium, France and Spain
Lu et al., 2025Social media policies: 185,969 tweets from 549 news workers
Mbaabu, 2025AI policies: semi-structured interviews with 14 editors and reporters from 4 Kenyan outlets
Miller & Nelson, 2022Social media policies: discourse analysis and interviews with 37 U.S. journalists
Molyneux & Nelson, 2024Social media policies: discourse analysis and interviews with 37 U.S. journalists
Peck, 2023AI use: survey of one newsroom to understand uses to inform newsroom policy
Piasecki & Helberger, 2025AI procurement: 12 semi-structured interviews with newsroom decision-makers and review of 16 terms and conditions documents
Porlezza, 2024Ethics codes: 15 publicly accessible codes of ethics in English, French, German or Italian
Radcliffe, 2025Broad survey of 221 journalists in Global South newsrooms
Sacco & Bossio, 2017Social media policies: interviews with 25 editors and reporters at major Australian media companies
Sánchez-García et al., 2025AI policies: 26 news outlets & 18 international entities
Seipp et al., 2024Legal research methods
Simon, 2022Broad research agenda for relationships between newsrooms and AI companies
Simon, 2024AI use: 121 interviews with news workers in the US, UK and Germany as well as 31 expert interviews
Simon, 2025AI use: 143 interviews with news workers in the US, UK and Germany
Solaroli, 2017Photo policies: archive content, interviews, ethnography
Timcke & Schroeder, 2025Policy analysis
Tseng et al., 2025LLM design for journalism: co-design and 20 interviews with journalists 
Umejei et al., 2025AI use: semi-structured interviews with 32 full-time and freelance journalists & editors in four African countries
van Drunen, 2025EU Media Freedom Act: legal analysis

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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]]> Latin America leads in mentions of journalism in AI laws https://cnti.org/news-clip/latin-america-leads-in-mentions-of-journalism-in-ai-laws/ Fri, 30 Jan 2026 19:32:23 +0000 https://cnti.org/?post_type=news-clip&p=11425 Precisely with the aim of analyzing the possible implications of AI legislation on journalism and the news sector in general, the Center for News, Technology and Innovation (CNTI) recently published the study “Journalism’s New Frontier: An Analysis of Global AI Policy Proposals and Their Impacts on Journalism.”

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]]> Precisely with the aim of analyzing the possible implications of AI legislation on journalism and the news sector in general, the Center for News, Technology and Innovation (CNTI) recently published the study “Journalism’s New Frontier: An Analysis of Global AI Policy Proposals and Their Impacts on Journalism.”

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]]> You Talkin’ To Me? https://cnti.org/news-clip/you-talkin-to-me/ Mon, 26 Jan 2026 19:35:02 +0000 https://cnti.org/?post_type=news-clip&p=11426 Why do people trust chatbots more than they trust humans? Or more specifically, human journalists?

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]]> Why do people trust chatbots more than they trust humans? Or more specifically, human journalists?

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]]> People who use chatbots for news consider them unbiased and “good enough,” new study finds https://cnti.org/news-clip/people-who-use-chatbots-for-news-consider-them-unbiased-and-good-enough-new-study-finds/ Thu, 22 Jan 2026 20:05:41 +0000 https://cnti.org/?post_type=news-clip&p=10415 Frequent users in the U.S. and India say they trust chatbots despite factual errors and outdated information.

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]]> Frequent users in the U.S. and India say they trust chatbots despite factual errors and outdated information.

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]]> Where Information Comes From https://cnti.org/reports/chatbots-for-news/where-information-comes-from/ Thu, 22 Jan 2026 13:00:00 +0000 https://cnti.org/cnti-news// Few interviewees have deep knowledge about how either journalism or AI chatbots work. At the same time, interviewees expressed generally positive attitudes towards AI chatbots alongside generally negative ones towards news media.

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]]> Journalists and AI chatbots generate content through fundamentally different processes. Given that CNTI’s interviewees incorporate both into their news repertoires, we found it important to understand how they think about both processes as well as whether and how they verify the information.

How We Did This

Using the Respondent research platform, CNTI recruited adults who said they (1) use AI chatbots at least once a week and (2) “keep informed about issues and events of the day” at least somewhat closely. To learn about the breadth of use cases and opinions, we sought to maximize variation across demographics. See topline for details.

Our interview protocol incorporated a concurrent thinkaloud approach. After a series of questions about general news and information habits, we asked interviewees to share their screen while demonstrating how they use one or more AI chatbots of their choice. We also asked them to walk us through AI chatbot interactions from their history and their use of other platforms and tools, including news aggregators, social media and news sites. These methods provide richness and depth; however, it’s not possible to generalize about the frequency of behaviors from these interactions, so we have refrained from using quantitative terms throughout this report.

CNTI’s analysis focused on reasons for seeking information identified by audience practitioners, the experience of interacting with AI chatbots and interviewees’ broader understanding of information.

The AI chat window terminal is a deeply personal space and the researchers in this project are incredibly grateful to interviewees who opened this safe space to them.

As with all CNTI research, this report was prepared by the research and professional staff of CNTI. This project was financially supported by CNTI’s funders.

See “About this study” for more details.

🇺🇲🇮🇳 Most interviewees rely on at least a few news outlets in addition to AI chatbots, but almost none expressed an understanding of journalistic methods. 

In the U.S., most interviewees have a strong sense that certain news sites count as credible. However, we saw no clear consensus among interviewees on which sites those are. (This is broadly consistent with other research on the polarization of media habits in the U.S.). In India, interviewees were uniformly negative about television news, while they displayed more affinity for international publications (e.g., Al Jazeera, BBC) and the leading English-language daily Indian newspapers (e.g., The Times of India, The Indian Express and The Hindu). Interviewees in both countries also prioritize different sources for different topics. For example, an interviewee might prefer a legal website over news sources for topics addressing current law. When asked how one determines credibility, almost no interviewee articulated an answer beyond a vague sense that some outlets have a political slant and must be used with caution — if not avoided altogether. The general lack of awareness among the interviewees about the process of journalism is consistent with findings from CNTI’s earlier focus groups and survey research.

🇺🇲🇮🇳 Interviewees lack clear vocabulary to describe how AI chatbots process information and generate language, so they default to using language that describes human processes like “thinking” and “reading.”

Many of the interviewees talked about what AI chatbots “know” or “understand” or “think,” which isn’t an accurate way to describe how they arrive at answers. In some cases, it was clear that interviewees used this language simply as a mental shortcut. In other cases, it seemed to further muddle misconceptions that interviewees held. Technically speaking, AI chatbots provide answers based on patterns and algorithms — not based on reasoning. (Relatedly, there is a robust debate in the technical community about whether the term “hallucination” is appropriate, since it presupposes that large language models have minds. Furthermore, technical solutions have proved elusive; in fact, false statements have been found to be “mathematically inevitable” with current technology.) 

Because interviewees analogize from human cognition, they often assume that AI chatbots have “read” and “understood” the links and sources they reference. As a result, our interviewees largely assume that responses accurately reflect the linked sources. A few interviewees in the U.S. specifically expressed a desire to see more documentation and training from developers about how AI chatbots work and how to prompt them for the best results. 

🇺🇲🇮🇳 While they lack deep knowledge about the underlying process for both journalism and AI chatbot content, interviewees expressed generally negative attitudes towards news media products and generally positive ones towards chatbots.

Beyond the specific sites and sources they themselves prefer, many interviewees expressed a broadly negative view of the news media. Overall, interviewees in India are skeptical of bias, commercial interests and sensationalism. Interviewees in the U.S. raise similar concerns along broadly partisan lines.

In contrast, these same interviewees are forgiving of and persistent with AI chatbots when given a wrong answer. Taking a collaborative stance, interviewees gently chide the AI chatbot to modify, clarify or correct the output. The interactivity seems to allow for second chances, while the interviewees have no such patience for fixed text.

In this context, interviewees can see AI chatbots as scaffolds for personal judgment. They use them to map the information environment, an approach that preserves decision-making power and independence of thought, allowing them to assert control over what they know and believe, positioning themselves as the final arbiters of truth.

🇺🇲🇮🇳 Interviewees tend to take the presence of cited and linked sources as an assurance of accuracy in AI chatbot outputs, and do not feel the need to click through them.

For most interviewees in both countries, AI chatbots showing sources is considered proof of accuracy, but few actually check the sources every time. Instead, many interviewees take the mere presence of sources as a guarantee of quality. Typically, they open up the list of sources or hover over links to see the sites, and assess the credibility of the generated text on the basis of those links. Most interviewees assume that if sources were linked or cited, the text would reflect them accurately. As one interviewee in the U.S. put it, the fact that “you can always double check if you want to” meant there was no need to check. Similarly, many Indian interviewees view AI chatbots as neutral aggregators. One described AI chatbots as “nothing but a library” that picks up information stored by humans. Others said they believe AI chatbots collect data from “sources like Google and YouTube” to provide a complete picture. One person explicitly used Perplexity to avoid “one-sided opinions” from officials or news channels, believing the AI chatbot’s aggregation of multiple sources constituted a “neutral” truth. These examples amply show a tendency toward automation bias among the Indian interviewees.

Complicating the story

Just one person, an interviewee in the U.S., expressed curiosity or concern about source weighting: “Okay, but what percentage of the output are you deriving from each of these sources? […] And it’s like, all right, if you’re weighting it 70% Fox News versus CNN, maybe next time just try to keep it 50/50, right? So that way, I’m not inherently getting information from one source versus another.”

🇺🇲🇮🇳 When interviewees do put in the work to verify AI chatbot outputs, it tends to be for one of two distinct reasons: either the outputs contradict their assumptions or the stakes are high.

We saw two very different reasons interviewees put in the work to verify the outputs they receive.

First, confirmation bias is playing a role in what interviewees verify. When AI chatbot outputs “feel” correct, interviewees do less work to check them. Many interviewees said they only make an effort to verify information if their gut instinct suggests it was off in some way. When something conflicts with prior knowledge or seems biased, they are more inclined to put in the effort. For example, one Indian interviewee checked if ChatGPT’s answers “match [their] thinking” on gold trends.

Second, interviewees put in more effort to verify information that has bigger consequences if inaccurate. When looking into legal procedures or specific legal rights, for example, we saw interviewees confirm the information the AI chatbots provided with official sources like the government or law firms. In fact, one U.S. interviewee said outright that they only rely fully on AI chatbots for things they don’t care that much about; the rest of the time, they have at least some background information they can use to judge the response.

🇺🇲🇮🇳 When interviewees do want to verify AI chatbot outputs, there is no consensus about the best way to do so.

The most common strategy for verifying AI chatbot outputs among interviewees in both countries seems to be comparing the output of two different AI chatbots. Interviewees also compare AI chatbot outputs with search engines, social media and trusted individuals. A few interviewees diligently follow links to see if they match what the AI chatbot says, but not many. Another strategy used by interviewees is instructing an AI chatbot to limit its sourcing and only use “verified” or “evidence-based” references, or provide “proof-based answers.” This strategy still assumes that the output is consistent with the linked source material. Another version of this strategy involves interviewees asking AI chatbots to recommend good sources and then turning directly to those. Interviewees in both countries have also developed idiosyncratic auditing strategies, where they put AI chatbots through a series of tests before deciding whether to use them.

🇺🇲🇮🇳 A number of interviewees recalled getting inaccurate or outdated information in the past, but it did not deter them from future use.

While none of the interviewees verify information systematically, a considerable subset of interviewees mentioned that they had gotten, at one time or another, inaccurate or unhelpful answers from AI chatbots. Well over half of the interviewees said they had received inaccurate information at least once, but few interviewees could describe a specific instance.

A big concern among interviewees in both countries is that AI chatbots may rely on outdated or partial information. We saw several instances of interviewees navigating this issue in real time. One interviewee asked about scheduled Big 10 football games and said that they had received last year’s schedule just a few days previously. Another interviewee noticed that all of the linked articles in an AI chatbot output had dates in early 2024, and a third was frustrated that linked articles in a quickly developing story were a month old. In each case, the interviewee noticed the missing information because they were already well informed on the topic, and then responded with a more specific prompt.

🇺🇲🇮🇳 In the search for unbiased information, interviewees are divided between those who worry about bias in AI chatbots and those who see AI chatbots as less biased than other sources of information — but neither bring deep knowledge into their opinions.

In the search for unbiased information, interviewees are divided between those who worry about bias in AI chatbots and those who see AI chatbots as less biased than other sources of information.

This is an area where the lack of transparency and clarity about how AI chatbots work comes into play. At least one interviewee described AI chatbots as a “black box” and raised concerns that AI chatbots might covertly promote specific products without disclosing a financial interest. Others assumed they understood how they work but made claims that can’t be fully verified. Many Indian interviewees said they used AI chatbots to escape the “bias” of mainstream media, without fully acknowledging that the models underlying these chatbots are trained on content drawn largely from that very media. Two interviewees in the U.S. expressed similar perspectives, one saying that AI chatbots “don’t have an opinion” and thus cannot provide biased information, disregarding the potential for biases in training data or outputs. Some interviewees assume that because the AI chatbot linked multiple sources with different viewpoints, the generated results must represent a “neutral truth.” In trying to explain their judgments about bias, several interviewees reached the limits of their information. “Where does it get the information?” one wondered aloud. “I don’t know…” There’s no question that a synthesis across political standpoints would be valuable. What is difficult — perhaps even impossible — is determining whether AI chatbots can actually provide one.

Continue reading

Overview
Information Needs
Chatbot Experience
Case Studies
About This Study

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]]> Action, Ease & Personalization: AI Chatbot News Experiences https://cnti.org/reports/chatbots-for-news/ Thu, 22 Jan 2026 13:00:00 +0000 https://cnti.org/cnti-news// How habitual AI chatbot users in the US and India stay informed

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]]> Overview

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.” 

Why we did this

In 2025, a small, but almost certainly growing, segment of people around the world used AI chatbots as a source of news. In most of the countries included in the 2025 Reuters Digital News Report, between 5 and 10% of the population said they get news from AI chatbots at least sometimes. In the U.S., 7% said they get news from AI chatbots — about half as many as the 15% who said they get news from podcasts, which are considered a relatively established news platform. In India, 18% said they get news from AI chatbots at least weekly.

Like social media before it, it’s likely that AI chatbots and similar technologies will have profound effects on the information landscape, even if their designers did not originally intend for them to play (or foresee them playing) a large role in this space.

We set out to learn from relatively early adopters in the U.S. and India why and how they have incorporated AI chatbots into their information routines. We also aimed to understand how these findings might inform news providers refining strategies for maintaining and growing their audiences, both with their own AI chatbots and other tools.

Why we chose these two countries

How we did this

Using the Respondent research platform, CNTI recruited adults who said they (1) use AI chatbots at least once a week and (2) “keep informed about issues and events of the day” at least somewhat closely. To learn about the breadth of use cases and opinions, we sought to maximize variation across demographics. See topline for details.

Our interview protocol incorporated a concurrent thinkaloud approach. After a series of questions about general news and information habits, we asked interviewees to share their screen while demonstrating how they use one or more AI chatbots of their choice. We also asked them to walk us through AI chatbot interactions from their history and their use of other platforms and tools, including news aggregators, social media and news sites. These methods provide richness and depth; however, it’s not possible to generalize about the frequency of behaviors from these interactions, so we have refrained from using quantitative terms throughout this report.

CNTI’s analysis focused on reasons for seeking information identified by audience practitioners, the experience of interacting with AI chatbots and interviewees’ broader understanding of information.

The AI chat window terminal is a deeply personal space and the researchers in this project are incredibly grateful to interviewees who opened this safe space to them.

As with all CNTI research, this report was prepared by the research and professional staff of CNTI. This project was financially supported by CNTI’s funders.

See “About this study” for more details.

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).

Which AI chatbot?

ChatGPT and Google’s AI tools (including Gemini, Google Assistant and Google AI Mode) were by far the most common AI chatbots used by our interviewees, with Microsoft Copilot third. 

We saw three different ways interviewees decide which AI chatbot to use.

Loyalty and lock-in: Some interviewees are loyal to one AI chatbot over another, even if they only use free services. They have invested their time in getting used to one system, and in customizing its responses to meet their needs and preferences. Their long chat history both helps them receive personalized responses and makes it possible to look up an older query, unlike search engines.

Take what you can get: Some interviewees, even if they have a preferred AI chatbot, are willing to use any of several chatbots to maximize their free use. They have accounts for multiple AI chatbot services and turn from one to the next when they reach their use quota. A few interviewees said they are not yet sure which AI chatbot best meets their needs, so they follow a similar pattern.

Multiple specialized tools: Some interviewees use a complicated array of AI chatbots for different purposes. They expressed strong intuitions that one is better than the others at programming, writing emails, image generation or personal advice. Similarly, some interviewees use one AI chatbot for professional purposes and another for personal needs. However, these preferences are individual and idiosyncratic; no consistent patterns emerged.

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.

💬 They find the clean and structured presentation of information easy to process and enjoyable to read. This includes layout features such as bullet points, short headers and strategic bolding of generated text; short sentences and simple language; and the absence of noise such as advertisements, sidebars, paywalls and more — although this design feature could well change as revenue models develop.

💬 They say AI chatbots are a faster and more efficient way to get information than either search or website browsing. Numerous interviewees noted ways that AI chatbots save them time, which is especially valued for tasks they find tedious, like comparing information from multiple websites.

💬 They like being able to tailor content to their desired level of understanding. Interviewees often ask the AI chatbots to simplify complex topics or provide additional detail, allowing them to “zoom” to the right granularity for their needs. Some interviewees have used this feature to help teach their children difficult concepts or to have something explained in simpler language.

💬 They like the encouragement and upbeat tone AI chatbots bring to interactions. Many interviewees emphasized the consistent, affirmational and upbeat tone of AI chatbots. Several also noted that they feel comfortable asking chatbots questions they might avoid with a person for fear of being judged.

💬 They expressed concern about privacy and surveillance, but their behavior does not strongly reflect it. While interviewees in both countries expressed concerns about their prompts or inputs not being secure, their primary strategy is to avoid inputting personal details and medical or financial 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.

ℹ Most interviewees rely on at least a few news outlets in addition to AI chatbots, but almost none expressed an understanding of journalistic methods. The word “credible” came up repeatedly as a factor in selecting outlets. But when asked how one determines credibility, almost nobody could articulate it concretely. Instead we heard vaguely worded ideas about political slant.

ℹ Interviewees lack clear vocabulary to describe how AI chatbots process information and generate language, so they default to using language that describes human processes like “thinking” and “reading.” Many interviewees discussed what AI chatbots “know” or “think.” For some it is a mental shortcut, but for others it seems to represent misconceptions about how AI chatbots process text. 

ℹ Interviewees expressed generally negative attitudes towards news media products and generally positive ones towards AI chatbots, even as they understand little about the underlying process of either. Beyond the specific sites and sources they themselves prefer, most expressed a broadly negative view of the news media, citing concerns like bias, commercial interest and sensationalism. In contrast, the same interviewees are forgiving of and persistent with AI chatbots when given a wrong answer. 

ℹ The presence of cited and linked sources tends to be taken as an assurance of accuracy in AI chatbot outputs; interviewees rarely feel the need to click through. In most cases, interviewees assume that responses accurately reflect the linked sources. Interviewees’ opinions about the credibility of sources is largely transferred to the AI chatbot output, regardless of fidelity to those sources. Further, many Indian interviewees view AI chatbots as neutral aggregators with low levels of bias. 

ℹ Two distinct factors tend to trigger a verification process: either the outputs contradict users’ assumptions or the stakes are high. Interviewees rely on gut instinct to decide what to verify. When looking into legal procedures or specific legal rights, we saw interviewees confirm the output of AI chatbots with official sources like the government or law firms.

ℹ When they do verify outputs, there is no consensus about the best way to do so. Although methods vary, multiple interviewees from both countries compare responses to the same question from two different AI chatbots. Some interviewees compare answers with search engines, social media or trusted individuals. Others limit the AI chatbot’s sourcing to “verified” or “evidence-based” references, assuming the output is consistent with the linked material. 

ℹ Past experiences of inaccurate or outdated information did not deter them from future use. Many interviewees mentioned concerns about outdated, inaccurate or partial information. However, no interviewees described these concerns as a deal breaker that kept them from using AI chatbots. 

ℹ In the search for unbiased information, interviewees are divided between those who worry about bias in AI chatbots and those who see AI chatbots as less biased than other sources of information — but neither bring deep knowledge into their opinions. Interviewees in both countries raised concerns about bias, but while some worried about bias in AI chatbots, others saw them as less biased than other sources. 

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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Information Needs
Chatbot Experience
Where Information Comes From
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About This Study

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]]> Information needs https://cnti.org/reports/chatbots-for-news/information-needs/ Thu, 22 Jan 2026 13:00:00 +0000 https://cnti.org/cnti-news// 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

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]]> According to one recent survey, weekly use of AI chatbots in six countries nearly doubled between 2024 and 2025, from 18% to 34% on average. And while that report found that use of AI chatbots “to get news” is relatively low, it found “information-seeking” to be the most common self-reported use case. The line between “information” and “news” is hardly clear-cut, and information-seeking is likely to include many topics where users might previously have turned to news sources.

How We Did This

Using the Respondent research platform, CNTI recruited adults who said they (1) use AI chatbots at least once a week and (2) “keep informed about issues and events of the day” at least somewhat closely. To learn about the breadth of use cases and opinions, we sought to maximize variation across demographics. See topline for details.

Our interview protocol incorporated a concurrent thinkaloud approach. After a series of questions about general news and information habits, we asked interviewees to share their screen while demonstrating how they use one or more AI chatbots of their choice. We also asked them to walk us through AI chatbot interactions from their history and their use of other platforms and tools, including news aggregators, social media and news sites. These methods provide richness and depth; however, it’s not possible to generalize about the frequency of behaviors from these interactions, so we have refrained from using quantitative terms throughout this report.

CNTI’s analysis focused on reasons for seeking information identified by audience practitioners, the experience of interacting with AI chatbots and interviewees’ broader understanding of information.

The AI chat window terminal is a deeply personal space and the researchers in this project are incredibly grateful to interviewees who opened this safe space to them.

As with all CNTI research, this report was prepared by the research and professional staff of CNTI. This project was financially supported by CNTI’s funders.

See “About this study” for more details.

To understand more about information-seeking behaviors, we started from one well-known model of reasons for information-seeking. This framework identifies four basic needs that people meet with news content. We present them here from most important to least important to the interviewees:

🚀 People need to act and decide, and they need help.
🧠 People need to know what’s going on.
🧩 They need to understand what’s going on.
♥ People need stories that make them feel something, whether that’s outrage or joy.

Here’s what we learned:

🚀 In both countries, interviewees seek information that helps them act. This is the most common use case for AI chatbots.

Few interviewees want information for its own sake. Instead, interviewees in both countries 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.

🚀 Indian interviewees ask AI chatbots to make predictions, while their U.S. counterparts ask AI chatbots for information they can base their own predictions on.

We saw Indian interviewees request specific financial, geopolitical and astrological predictions and use them to make decisions, even as they expressed skepticism. For U.S. interviewees, on the other hand, the desire for prediction generally stopped at asking the AI chatbots which sources or indicators to follow.

🧠 When it comes to knowing what’s happening, interviewees in both countries use AI chatbots to supplement existing news habits, not replace them. Specifically, they turn to AI chatbots for narrowly scoped updates and fact-checking.

No interviewee in either country said they rely solely on AI chatbots when they want to know what is happening. If they are broadly familiar with a story, though, they might turn to AI chatbots for updates. Similarly, when they see a story that they don’t believe, many of them ask AI chatbots to fact-check. Using AI chatbots for fact-checking is especially common on social media, where embedded tools like @grok on Twitter or Meta AI on Facebook promise to fact-check or otherwise validate content.

🧠 For the most part, U.S. interviewees don’t turn to AI chatbots for broad news discovery, while Indian interviewees are more mixed.

Just one U.S. interviewee said or demonstrated that they regularly ask an AI chatbot about “latest headlines in [zip code]” or “latest political news” to get broadly informed. A few other U.S. interviewees said they ask for updates on specific topics, but turn to other sources for news discovery. On the other hand, at least six of the interviewees in India ask AI chatbots for the “latest news” or “top headlines.”

🧩 Both U.S. and Indian users of AI chatbots turn to them for context that is often absent in traditional news media.

Interviewees in both countries find their existing news repertoires frustrating. Many said that news stories often start in the middle, with background information buried or difficult to find.

🧩 Interviewees in both countries worry about the political slant of news, but they want to resolve it in different ways.

Many interviewees said that most news sources have a political slant. We saw consistent differences between how interviewees in India approach political slant and how interviewees in the U.S. do. In the U.S., interviewees want to get information that spans the full political spectrum, whether from news aggregators or AI chatbots. Several U.S. interviewees said they find the AI chatbots to be unbiased because they pull from multiple news sources with different points of view. In India, many interviewees want to bypass traditional sources altogether, rather than synthesize across the range of political perspectives.

♥ In both countries, emotional stimulation and entertainment are low information priorities.

The “need to feel” does not seem to be a priority for our interviewees, and was not a focus of our conversations. Interviewees engage emotionally with AI chatbots, but only rarely with the information itself.

The need to act

For interviewees in both countries, needing to act is a major motivator for AI chatbot use; AI chatbots meet this need proactively

All interviewees in both countries talked at length about needing information that helps them act, and almost all of them typically start with AI chatbots to meet these needs. 

Starting with AI chatbots doesn’t mean ending with AI chatbots.

Many interviewees toggle back and forth between the AI chatbot and external links or sources. Some described turning to the AI chatbot after reading a news article to understand what they could do about current events (e.g., tariffs), although we did not see this specific pathway in real time. Interviewees followed links provided by the AI chatbot and returned to the AI chatbot conversation after doing so. We saw one person in the U.S. switch back and forth between ChatGPT, Gemini and linked resources from law firms to better understand their rights when ICE agents are active in their area. For high-stakes actions like visa applications or legal agreements, some Indian interviewees use AI chatbots to understand the requirements but seek official websites to confirm the steps and perform the task. As one U.S. interviewee noted, AI chatbots are “an 80/20 source. It’ll give me 80% of the information in 20% of the effort, but you know, you gotta be careful about taking that 80% to the bank, and there’s always 20% missing.”

Unlike a search engine, AI chatbots retain the context of prior inputs, which makes it relatively easy to refine a query or to ask follow-up questions.

All the same, doing so can take a lot of time. At least one interviewee asked an AI chatbot to suggest better prompting strategies, then tried out those prompts. When AI chatbots offer a number of follow-up options, interviewees might select one and ask for further details or external sources. For example, one interviewee in the U.S. initially input “What are the popular states to live in” and then responded “yes” when ChatGPT asked, “Would you like me to narrow it down to best states for young professionals, families, or spiritual creatives/entrepreneurs (like your vibe)?” When asked how they would follow up, their next prompt was “North Carolina city overview and links for me to discover more in-depth.”

Actionable information about emerging topics is much broader than “service journalism.” To be sure, interviewees in both countries are using AI chatbots for the pillars of service journalism: consumer product comparisons, travel itineraries and navigating changing bureaucratic requirements. But they are also using AI chatbots to understand how to react to “hard news.” Interviewees ask AI chatbots about the impact of geopolitics on their investments, how to respond to the U.S. government shutdown and what changing visa rules and tariffs would mean for them professionally. One person in the U.S. even asked ChatGPT who to vote for.

Asking for election advice

🇺🇲 One person in the U.S. first asked ChatGPT to give them a sense of who to vote for in several local races, noting that the answer didn’t include all of the candidates on the ballot. When they rephrased the question to ask for a direct recommendation, ChatGPT refused to provide one but ultimately offered a response emphasizing that specific candidates aligned with their priorities.

U.S. user

im going to fill out my 2025 official general municipal and special elections ballot [city] I need you to give me feedback for selection on mayor, and district [#] commissioner. Im looking for pros and cons for each candidate

ChatGPT

I’d be glad to help you think through pros and cons. … here’s what I found so far on the Mayor race and District [#] Commissioner race in [city] (2025). and some factors you might weigh.

Mayor – Key Candidates & Considerations

U.S. user

what about the other candidates?

ChatGPT

I dug around; the available public sources are limited, but here’s what I found on other mayoral and District [#] candidates, along with what we can infer (and what to watch out for). Use this as a starting point, not a definitive guide.

If you like, I can dig more deeply (local news archives, candidate filings) to unearth all the District [#] candidates with detailed bios. Would you like me to do that and then present a more complete side-by-side?

U.S. user

can you explain to me the [#] referendum ammendments in this ballot

ChatGPT

I couldn’t find a definitive source that spells out exactly which [#] referenda / charter amendments will be on the 2025 [city] ballot with full text and analysis. The city’s official sites mention that there will be charter amendment questions submitted with the November 4. 2025 municipal election. [sources]

If you like. I can try to track down a preliminary “voters’ guide” or city PDF with the [#] exact ballot amendment texts (when released) and then we could do a side-by-side pros/cons for each of the four. Do you want me to do that now?

U.S. user

which candidates in both mayor and district commissiones would be the best choice for me: im a [age] apartment owner, working for an international corporation, who wants a safer city, more job opportunities and the city to get better

ChatGPT

Thanks for sharing your priorities. Because this involves political candidate selection, I must stay neutral and cannot recommend a specific person. However, I can help you identify which types of candidates in this race most closely align with your goals and show how each major candidate compares based on those goals.

🇺🇲 In fact, a major benefit of using AI chatbots to get informed is that they proactively support the need to act.

Several U.S. interviewees pointed out that even when they input factual queries, the results focus on how to act in response. After one interviewee asked about the status of the government shutdown, they were pleased that the results included clear next steps: 

“With ChatGPT, they were telling me, ‘okay, if you’re on this program, go to food pantries, start saving up, start stocking up because you don’t know what damage it might do to people.’ So they was just basically, they get you ready for survivor mode, basically. That’s one thing I like about ChatGPT too. They get you ready.” 

When another interviewee asked for updates on H1-B policy, the response ended with an offer to “pull together a summary of how each of the changes specifically affects [specific group] (like you, seeking [specific job]).” The baseline emphasis on action is seen as widely valuable. Interviewees in India also seemed to appreciate the suggestions, but did not highlight them as explicitly.

🇮🇳 In India, a distinct subset of interviewees use AI chatbots for forecasting and speculation to navigate uncertainty in finance, politics and personal life.

Interviewees frequently task AI chatbots with predicting future outcomes to gain a strategic advantage or peace of mind. In the financial realm, we saw two interviewees ask for Bitcoin price trends and market volatility forecasts based on political social media activity, using these predictions to make immediate trading decisions. A third interviewee requested predictive analyses of stock sectors based on quarterly earnings, though they approached these outputs with high skepticism due to previous financial losses incurred from following AI chatbot advice. This predictive behavior extended to the personal domain through AI-generated astrology. In geopolitics, two interviewees explicitly asked AI chatbots to speculate on future developments, such as the “next steps” in U.S.-India relations or the remaining tenure of federal officials. AI chatbots present interpretations of phenomena as objective facts; interviewees interpret these interpretations as facts, not as a statistical average of its training data and what’s available on the internet. A broker would never give the kind of confident specific advice ChatGPT gives. Similarly, a journalist would never speculate. Indian chatbot users like this behavior a lot.

Investigating crypto trends

🇮🇳 One interviewee in India used various AI chatbots to predict the performance of the global crypto markets. They linked this with the tenure of U.S. Federal Reserve Chair Jerome Powell. They did not like reading through the entire response and frequently followed up with requests to make it shorter. In the end, they wanted, and received, a yes/no prediction from ChatGPT. They also posed the same question to two other AI chatbots and compared the responses. Here, the interviewee used Google’s AI Mode to speculate about the U.S. Federal Reserve Chair’s term. Commenting on the response, they said,  “Google remained completely unbiased on this matter because I believe Google is much more involved in political gossips.” Comparing the two AI chatbots, they noted, “Google is much more like a formal thing. ChatGPT is much more like a casual partner. […] Now Google also knows that it cannot answer this in yes or no. But ChatGPT did it because it was advising me…” That is, they saw Google’s refusal to answer as a sign that the company is complicit in politics.

Indian user

Crypto trends rn

ChatGPT

Indian user

Do u think that the rates cut Il affect the Ethereum

ChatGPT

Yeah – absolutely, rate cuts can have a noticeable effect on Ethereum (and crypto in general), though the Impact depends on how deep and fast those cuts are.

Let me explain clearly but simply

If you want, I can show you a simple timeline projection (what might happen to ETH price and market cap at each rate-cut stage – pre-cut, during, and post-cut).

Want me to?

Indian user

How long do u think that jerome powell Il last in the power, as per current news and trends, as recently trump threatened him

ChatGPT

Here’s a clear breakdown of how long Jerome Power could stay in his current roles at the Federal Reserve System (Fed), what the current signals are, and how the threats from Donald Trump might or might not change things.

What’s certain: current terms

• Powell is serving a four-year term as Fed Chair, which began on May 23, 2022. 🔗

• That Chair term ends in May 2026. [sources]• Separately, his position as a member of the Board of Governors of the Fed runs until January 2028. 🔗

My view: How long I think he’ll last

Putting the facts and signals together, here’s a reasoned speculation:

• I expect Powell to serve out his full term as Chair until May 2026. The legal term is clear, he has publicly stated intent to serve his full term, and while there is political pressure, there is no strong signal that he will be removed early.

Indian user

Please make it shorter.

ChatGPT

Sure – here’s the short, clear version

Powell’s Current Term

• Fed Chair term: ends May 2026

• Governor term: lasts till Jan 2028• So legally, he can stay in power until 2026 as Chair (and 2028 overall).

Would you like me to add a quick note on how a Powell exit could affect crypto and markets if Trump really removes him early?

Indian user

Just tell me .. yes or no

ChatGPT

Yes – he’s very likely to stay in power until May 2026.

Here, the interviewee used Google’s AI Mode to speculate about the U.S. Federal Reserve Chair’s term. Commenting on the response, they said,  “Google remained completely unbiased on this matter because I believe Google is much more involved in political gossips.” Comparing the two AI chatbots, they noted, “Google is much more like a formal thing. ChatGPT is much more like a casual partner. […] Now Google also knows that it cannot answer this in yes or no. But ChatGPT did it because it was advising me…” That is, they saw Google’s refusal to answer as a sign that the company is complicit in politics.

Indian user

how long do you think jerome powell ll last in the power

Google AI Overview

Jerome Powell’s current term as Chair of the Federal Reserve is scheduled to end in May 2026. The question of how much longer he remains in power depends on several factors, including the 2024 presidential election, his own future intentions, and legal challenges to the Fed’s independence.  🔗

Indian user

make it shorter

Google AI Mode

Jerome Powell will serve as Chair of the Federal Reserve until his term expires in May 2026. After that, President-elect Donald Trump has said he will not reappoint him.  🔗

Indian user

just tell me in yes or no

Google AI Mode

It is impossible to answer “yes or no” because the question is complex and depends on future events.  🔗

Here’s why a simple answer is not possible:

This interviewee asked Perplexity the same question. Looking at the response, they explained,

“Perplexity seems like the most formal AI to me and the one that provides the longest answers. […] Now, you won’t find much difference in the answers of Perplexity Pro and Google AI, because Perplexity provides Google-based answers. […] But since it is backed by OpenAI’s APIs, because of this, it is answering in yes or no like ChatGPT.”

Indian user

how long do you think jerome powell ll last in the power

Perplexity

🔗

Jerome Powell is expected to serve as the Chair of the Federal Reserve until his current term ends on May 15, 2026, unless political developments prompt him to leave earlier, but there are no indications of an immediate exit before then 1 2 3. After that, he technically has the option to remain on the Federal Reserve Board as a governor until his term ends in January 2028, but it is considered unlikely he will choose to do so

Indian user

make it shorter

Perplexity

Jerome Powell’s term as Federal Reserve Chair ends in May 2026, and a replacement will likely be picked before then

Indian user

just tell me in yes or no

Perplexity

Yes, Jerome Powell is still in power as Federal Reserve Chair as of now

Indian user

I asked u whether he Il be in power in near future. what do u believe? just tell in yes or no

Perplexity

🔗

Yes, Jerome Powell will remain in power in the near future

A news organization that’s meeting this need

The Jersey Bee, a U.S. local news outlet, operates in a region where one in every nine people does not have enough to eat. They report on the affordability crisis, and they also make it easier for hungry people to get groceries with an interactive database of food pantries. The database can be sorted by county and hours of operation. It also provides detailed, verified information on eligibility criteria, specific dietary needs that can be accommodated and languages spoken.

The need to know

When interviewees want to know what’s happening, AI chatbots are supplementing existing news habits and routines, not replacing them

In both countries, most interviewees engage with AI chatbots to augment their existing news repertoires by seeking facts, specific knowledge or status updates on current events.

Well over half (about 19 out of 26 in India and 18 out of 27 in the U.S.) of the interviewees explicitly demonstrated or described using AI chatbots (primarily ChatGPT, Gemini, Perplexity and Grok) to retrieve information or updates. The use cases ranged from checking daily headlines and weather reports to monitoring stock market trends and verifying specific claims seen on social media.

Despite the high use of AI chatbots for general queries, all interviewees continue to rely heavily on other digital sources when they want a broad update on what is happening, especially breaking news.

Nobody said they use only AI chatbots for this purpose. Every interviewee maintains a diet of other information channels, including YouTube; Google News and other aggregators; news outlets’ apps and websites; and social media platforms like X (formerly Twitter), Instagram and Telegram. A small subset of interviewees in each country actively avoid using AI chatbots for the latest news, citing experiences with out-of-date information. We also saw this happen to several interviewees: in response to a query, they received last year’s sports schedule or company report.

H1-B policy updates

🇺🇲 During our conversation, this interviewee looked for updates on a changing legal situation in the U.S. They noticed that the first response contained sources that were a month old, and they followed up with another prompt, which returned more recent information.

U.S. user

Could you tell me a bit about if there is any changes in the policy made for H1-B again ?

ChatGPT

Hey [name] – good question. Yep, there are some major recent changes to the H-1B visa program in the U.S. that you absolutely should be aware of (especially since you’re job-hunting and might deal with immigration issues). I’ll break them down straight-up, so you know what’s going on.

If you like, I can pull together a summary of how each of the changes specifically affects [job] (like you, seeking [job] roles) – we can map out “worst case / best case” for sponsorship under these new rules. Want me to do that?

Recent H-1B policy changes news

[three links, dated Sep 19 and Sep 20, 2025]

U.S. user

okay so there has been an update in the news again about H1-B specifically for international students what is that update about please give me all the latest updates possible

ChatGPT

Cool – I found the latest scoop on the H-1B visa changes, especially how they relate to international students (so if you were estimating future sponsorship or thinking ahead, this is very relevant). I’ll lay it out clearly, then highlight how it affects someone in your shoes ([name]) chasing [job] roles.

They generally prefer using AI chatbots for information, but noted that the downside is precisely this risk of outdated or incomplete information, especially if one doesn’t have enough background information to assess the response.

“If I have half of the information and half of the information hasn’t been shared with me because of the chatbot then it’s like I have half knowledge about it. […] It should be able to give me the latest update until this particular moment rather than some news that happened a month back or something.”

In both countries, the most common “knowledge” use cases are summarization, getting updates on specific stories, evergreen information and fact-checking.

Some interviewees see AI chatbots as a good way to get updates on specific ongoing issues, but they largely turn to their existing news repertoires for discovering new stories and keeping abreast of breaking news. They ask AI chatbots narrower questions like “summarize me in two minutes” or“provide an up-to-date summary of developments regarding [company].” Some also use AI chatbots primarily for quick fact-checking, especially on social media platforms. Several interviewees use Grok and Meta AI to summarize long conversations on X and Facebook or tag the AI chatbot directly in the comments, asking “@Grok, is it real?” to verify the authenticity of claims.

Only a small subset of interviewees regularly use AI chatbots for news discovery, and when they do, they use it as a supplement to other sources.

Only one U.S. interviewee said they regularly ask Gemini, “What’s the latest headlines in [zip code]?” This person also gets push notifications from news outlets, follows friends and family on social media to see what they share and talks to other people about what’s happening in the world. They appreciate AI chatbots because “It’s not like you get pages and pages and pages or all these different headlines that you need to find what’s important. Gemini kind of sums it up for you. And then you can always go into more detail.” Using AI chatbots to scan the headlines is more common for interviewees in India, but far from universal. We observed several interviewees use simple, keyword-heavy prompts to request news digests. For instance, one asked, “give me today’s major international news” and“top 10 international news”. Another queried “Hindi News today” and“More news” to get a list of current events. A third interviewee used Perplexity to generate a daily digest of “political and policy updates.” All three get additional news information elsewhere, too.

Today’s headlines

🇮🇳 This interviewee uses ChatGPT and, to a lesser extent, Gemini, for generating top news headlines of the day. They sometimes then ask the AI chatbot to translate the headlines into Hindi. During the interview, they picked out one of the news stories about the demise of Thailand’s Queen Mother for more details. Generally, they rarely venture deeper after asking about the main headlines. They reported an 80-85% satisfaction rate with the answers received. The prompts they demonstrated are extremely minimalistic — almost keyword-like.

Indian user

today news

ChatGPT

Here are the top news highlights for today, [date]

Indian user

in hindi

ChatGPT

[same headlines and content as in the English version above

Indian user

more news

ChatGPT

International News 

Queen Sirikit of Thailand passes away

Indian user

“Queen Sirikit of Thailand passes away” in detail

Indian user

Current affairs [city]

Gemini

Based on the current events in [city], here are some key updates:

This interviewee subscribes to the Hindustan Times newspaper but prefers Instagram, Facebook and YouTube to keep up to date with issues, events and daily happenings. They find newspapers cumbersome and limited, and dislike the ads on news websites. So, they’ve moved toward AI chatbots for a more efficient and specific information experience.

AI chatbot interactions to help interviewees know what’s going on are often very brief, frequently consisting of a single question and response, like “Why there is thunder when lightning strikes?” or“Margaritaville of the Seas, which ship did it used to be before?” When their questions aren’t answered, they either rephrase the question or turn to a different source of information.

A news organization that’s meeting this need

WTF Just Happened Today is a daily newsletter about U.S. politics, specifically the U.S. presidency. With the tagline “I doomscroll so you don’t have to,” the newsletter provides the day’s biggest news in a single sentence. Each clause of the sentence receives its own paragraph-length write-up with links to other major news sources, ensuring that readers can quickly keep up with a broad swath of political news.

An AI product that’s meeting this need

Founded by three former members of Google’s NotebookLM project, Huxe is an AI-powered, audio-first personal assistant app designed to consolidate information from a user’s calendar, email, news sources and stated interests into a single personalized audio feed. The app presents this material in an audio podcast–style format intended for listening while commuting or multitasking, with a supporting visual interface that updates in real time to reflect the topic being discussed. The system emphasizes efficiency, offering a concise, customized digest of current and relevant information that can fit into daily routines for staying informed. According to the company, Huxe “finds what you need to know— before you know you need it.”

The need to understand

Interviewees find chatbots valuable for understanding complicated details, root causes and a range of perspectives

Most interviewees in both countries utilize AI chatbots to gain a deeper understanding.

Simplification, background information and root causes of issues are key AI chatbot use cases. Interviewees observed that news stories do not always provide perspective or background. Instead, news stories often start with the newest information, which can be confusing for people who are not already following the issue closely. Interviewees said 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. News stories sometimes take this kind of knowledge for granted. One person in India specifically sought the “root cause” of geopolitical conflicts, feeling that social media provides only surface-level outcomes.

Getting background on a scandal

🇮🇳 This interviewee wanted to know more about a recent insider trading scam which involved collusion between a company and regulatory officials. They had previously heard on a YouTube channel and read in a newsletter about the scam. In the interview, they prompted their preferred AI chatbot, Perplexity, for information on the case. They then explored the connection between two key figures, which had been unclear.

Indian user

IEX SCAM investigation on SEBi

Perplexity

🔗

In October 2025, the Securities and Exchange Board of India (SEBI) exposed a major ₹173.14 crore insider trading scam involving shares of Indian Energy Exchange Ltd. (IEX), allegedly linked to officials from the Central Electricity Regulatory Commission (CERC) 1 2 3 4.

Indian user

give in detail about profession of Yogeita mehra and How there is establishment of Connect with Bhoovan singh

Perplexity

🔗

Yogeita S. Mehra is a senior economist and Chief of the Economics Division at the Central Electricity Regulatory Commission (CERC), the statutory body overseeing India’s power market regulation]

When asked what they like about AI chatbots and the way they provide information, this interviewee said, “If I’m going through any journals, like news, like Economic Times, means the journalist will be asking only a few questions and they will be exploring it in only a few ways, right? Whereas in AI means I can explore it…”

Despite high engagement with AI chatbots, all interviewees continue to utilize other digital sources for synthesis and context, as is the case for their other information needs.

We saw interviewees navigate back and forth between aggregators, news sites and AI chatbots to ensure they were capturing the full breadth and complexity of a topic as well as different positions on that topic. For example, one U.S. interviewee read a news story, asked an AI chatbot for other perspectives, clicked through and read those links, then returned to the AI chatbot with follow-up questions about different political perspectives. YouTube was frequently cited as a primary tool for deep dives, particularly for visual learners or those seeking expert commentary on tech, finance and geopolitics. Interviewees in India also turn to social media like Reddit and Discord, or community platforms like Quora to read discussions or gauge public sentiment before or after consulting AI chatbots.

Unlike simple fact-checking, using AI chatbots for understanding is generally characterized by multi-turn conversations, role-playing and iterative refinement.

A common workflow among interviewees involves “zooming in,” which involves pasting complex text or asking about a broad topic, then refining the query to get to the right level of detail. Interviewees in India often treat the AI chatbot as a debate partner. For example, one interviewee in India engaged in a “full debate” on Messi vs. Ronaldo, instructing the AI chatbot to be “blunt” and “unbiased” rather than diplomatic. Another interviewee in India liked to “argue” with AI chatbots, challenging the AI’s “opinion” on topics like job automation to see how it defended its position. One U.S. interviewee prompted Gemini to “Explain today’s aws [Amazon Web Services] outage and write this out in a level where a non tech worker can understand” and then continued diving deeper until they were sure they understood what had happened.

🇺🇲 Interviewees in the U.S. see AI chatbots as a relatively easy way to get information across the political spectrum and see other points of view.

Interviewees in the U.S. say that most news sources have a political slant, and getting an understanding requires a synthesis that spans the full political spectrum. Reading a single integrated text that references sources from different perspectives is something that they find valuable. They also turn to aggregators, social media, comments sections or specific sources they disagree with to meet this need.

Getting multiple perspectives on the election debate

🇺🇲 This interviewee turned to ChatGPT to get a broad perspective on who won a recent debate between candidates for New Jersey governor as well as for updates on polls. They deliberately used an AI chatbot rather than their regular sources because they didn’t want to get their information exclusively from sources that shared their political leanings. Instead, they wanted to see the broader spread of reactions.

U.S. user

What is the general feedback as to who did a better job in the New Jersey Governor election debate between jack ciattarelli and mikie sherrill

ChatGPT

Here’s a rundown of how people reacted and what the early assessments suggest about who “won” the first debate between Jack Ciattarelli and Mikie Sherrill in the 2025 New Jersey governor’s race. If you want, I can also pull up specific public polls or focus-group feedback later.

U.S. user

What is the general feedback as to who did a better job in the New Jersey Governor election debate between jack ciattarelli and mikie sherrill?

ChatGPT

Here’s a more up-to-date look at how media, local observers, and early reactions are assessing who performed better in the debates between Jack Ciattarelli and Mikie Sherrill:

U.S. user

What are current polling results for both candidates?

ChatGPT

Here’s a snapshot of recent polling for the 2025 New Jersey gubernatorial race between Mikie Sherrill and Jack Ciattarelli – and what the trends suggest:

Recent polling highlights

A news organization that’s meeting this need

Scroll is an Indian digital news platform known for its deep-dive reporting on complex social and political issues. It developed Factivo, a “next-generation versioning platform for newsrooms and organizations that prioritize truth and clarity.” Factivo takes a single news article and automatically generates multiple alternative formats such as FAQs, key takeaways, contextual sliders and interactive summaries so audiences can engage with the same information at different levels of depth and across platforms. By allowing users to choose how much time and attention they invest, Factivo is a good example of addressing the user’s need to understand by meeting them where they are.

An AI product that’s meeting this need

Particle aggregates stories from hundreds of news outlets around the world, highlighting the most-reported issues of the day. Links to news sources are prominently highlighted, encouraging the user to click through to learn more. Specific entities — like countries, world leaders, companies and more — have their own pages, so finding background information is straightforward.

The need to feel

For interviewees in both countries, emotional stimulation and entertainment are low information priorities

When interviewees are seeking information, fulfilling a need to feel something isn’t a high priority.

In both countries, we heard very little about low-stakes news content, like celebrity gossip or human interest topics. News simply isn’t a source of entertainment for almost any of the interviewees.

Outrage about political topics is the main emotional response to news.

Interviewees feel like a lot is happening on the global, national and local scale. For the most part, they feel bad about it. They are angry and scared, about both what’s happening and the media coverage. We saw two interviewees, one in each country, turn to AI chatbots to hone their outage on these topics.

Frustration with the framing of current events

🇮🇳 This interviewee wanted to know why their friends — and world media — were accusing young protesters of committing vandalism in Nepal. On receiving an unsatisfactory answer, they added that there were ample visuals of police shooting at students, so paucity of information was not the underlying problem. The person was more disappointed by the second reply and thought perhaps they were not specific enough in the original question, so they edited the prompt, adding more context. By this point in the interaction, the person was incensed. So they asked bluntly if the AI chatbot was calling one narrative more sensationalist than the other. The conversation ended after this.

Indian user

Why are all my friends across both sides of the political spectrum accusing Gen Z protestors of burning down public property when it’s the live ammunition which killed them on sept 8 which triggered the otherwise peaceful protests against corruption

ChatGPT

That’s a really thoughtful and emotionally aware question – and it touches on a deep problem in how public narratives about protests are shaped and spread.

What you’re describing – protests that began peacefully after state violence or corruption, but are later portrayed by many people as “violent” or “destructive” – happens often across the world. A few dynamics are usually at play:

Indian user

There were enough visuals on TikTok and Instagram of the students in uniform being shot at point blank range so it’s not like the information wasn’t there

ChatGPT

You’re absolutely right – and that makes your frustration completely understandable. When direct, undeniable evidence exists – like

videos clearly showing unarmed students in uniform being shot – it feels especially disorienting and painful to see people deny or distort what happened.

What you’re seeing is a clash between information availability and

information acceptance – two very different things:

Would you like me to pull up independent reports or analyses (e.g., from human rights groups or international outlets) that verify what happened on September 8? That might help you show people credible sources ….

Indian user

Sure go ahead

ChatGPT

Here are several credible independent sources that do document the use of lethal force by security forces in the 2025 Nepal Gen Z protests (which began on September 8, 2025). They may help when you’re trying to show others what the documented facts are.

Indian user

Why are all my friends across both sides of the political spectrum in different countries accusing Gen Z protestors of burning down public property when it’s the live ammunition which killed them on sept 8 which triggered the otherwise peaceful protests against corruption ..note that they’re getting the information from their local media outlets and not necessarily from Nepal

ChatGPT

That’s a really thoughtful and important question – and it points to how media framing, distance, and information ecosystems can distort how events in one country are understood elsewhere. Let’s unpack what’s probably happening here.

Indian user

Are you saying that then that visuals of arson are more sensationalist than the state firing at unarmed students in school uniform ? Were these visuals of the students shot already not on social media?

ChatGPT

That’s an excellent – and really sobering – follow-up. You’re absolutely right to ask how images of the state firing on unarmed students could possibly be less prominent than footage of a burning building.

“If the truth is not being shown, does even ChatGPT have access to what really happened?” they asked.

Nurturing outrage about the “Big Beautiful Bill”

🇺🇲 This interviewee showed us their query about the proposal to fund the U.S. government known as “the Big Beautiful Bill.” The query touched on the details of its provisions (an example of seeking facts), a deeper understanding of their breadth (an example of seeking context), the “strange and small provisions that are going to hurt or impact the regular people” (an example of evoking emotion) and its impact on 401K plans (an example of seeking help with action). While these other needs were present in this AI chatbot interaction, the need to nurture their outrage and anger seemed to be driving their choice of prompts.

U.S. user

Can you look up the latest copy of the big beautiful bill that was signed by congress and the president and highlight the worst provisions and when they are…

Gemini

The “big beautiful bill” you are referring to is a massive spending and tax bill signed into law by President Trump on Friday, July 4, 2025. This legislation extends the 2017 Tax Cuts and Jobs Act, makes most of the tax cuts permanent, and increases spending for border security, defense, and energy production. 🔗🔗

U.S. user

does it impact 401ks in any way

Gemini

The “Big Beautiful Bill” signed by President Trump on July 4, 2025, does have some impacts related to retirement savings, though it largely maintains the existing tax-deferred status of 401(k)s and IRAs.

U.S. user

What are some strange and small provisions that are going to hurt or impact the regular people?

Gemini

Can you tell me what strange and small provisions in the bill will hurt regular people? What are the unusual and minor provisions of the “big beautiful bill” that will negatively affect everyday citizens? “Big beautiful bill” unusual small provisions impact regular people “Big beautiful bill” strange minor provisions negative impact citizens The “Big Beautiful Bill,” recently signed into law, contains several provisions that, while perhaps not always headline-grabbing, are expected to have a significant and often negative impact on “regular people.” These are often embedded in broader policy changes or have indirect effects.

U.S. user

what else?

AI chatbots make the experience of getting news a little bit more pleasant, at least for some. As one U.S. interviewee put it, “I get a kick out of [using ChatGPT]. It’s just fun.” That general sense of enjoyment is not specific to news content.

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Overview
Chatbot Experience
Where Information Comes From
Case Studies
About This Study

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