{"id":8558,"date":"2023-08-25T10:00:00","date_gmt":"2023-08-25T14:00:00","guid":{"rendered":"https:\/\/cntiwpedev.wpenginepowered.com\/?p=8558"},"modified":"2025-10-06T14:54:37","modified_gmt":"2025-10-06T18:54:37","slug":"enhancing-algorithmic-transparency","status":"publish","type":"post","link":"https:\/\/cnti.org\/issue-primers\/enhancing-algorithmic-transparency\/","title":{"rendered":"Enhancing Algorithmic Transparency"},"content":{"rendered":"\n
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CNTI’s Summary<\/h2>\n\n\n\n

Digital platforms have become central to how people around the world find and share news and information. Currently, each platform operates under its own rules and conventions, including what content is shown and prioritized by algorithmic infrastructures and internal company policies. Establishing legal and organizational policy to promote algorithmic transparency is one critical step toward a structure that allows for more accountability for often-opaque digital platform practices related to content selection and moderation processes. Various stakeholders \u2013 including policymakers, researchers, journalists and the public \u2013 also often have different purposes and uses for transparency, which need to be thought through to be sure it serves those needs while protecting against the risks of political or commercial manipulation. These considerations also carry through to who is designated to regulate transparency requirements.<\/p>\n\n\n\n

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The Issue<\/h2>\n\n\n\n

The internet is enmeshed in virtually every aspect of modern life. Most often, the public\u2019s online experiences flow through digital platforms, which serve as intermediaries between the content being delivered and the individuals consuming it. That provides convenience but also raises serious concerns. A central one is how much control digital platforms have, or should have, over what people see, especially when it comes to news<\/a> and information, and how transparent digital platforms\u2019 decision-making processes, typically conducted through the use of algorithms, needs to be.<\/p>\n\n\n\n

Each digital platform, and its algorithmic infrastructure, operates under its own rules and conventions to determine which content is and is not allowed<\/em> as well as what content is prioritized<\/em> on the platform. Algorithmic choices have far-reaching implications ranging from determining user experiences by selecting one news story over another to facilitating hate<\/a> speech, disinformation<\/a> and violence<\/a>. <\/p>\n\n\n\n

Policymakers, journalists, social media researchers, civil society organizations, private corporations and members of the public are raising awareness of and questions about what are often-opaque digital platform practices related to content selection and moderation processes. The global news industry in particular \u2013 which relies to a significant degree on digital intermediaries to reach<\/a> audiences \u2013 has articulated the need for a better understanding of how algorithms rank, boost, restrict and recommend content (including, but not limited to, news) and target consumers. Without a better understanding of how these algorithms are currently built, it is difficult for publishers to work within their constraints to reach audiences or to play any role in determining how algorithms should be built to promote an informed public.<\/p>\n\n\n\n

Thus, in addition to ensuring that algorithms are designed to identify fact-based, independent journalism (a topic CNTI addresses in a separate issue primer<\/a>), it is critical to enhance transparency in and accountability for how digital platform algorithms function.<\/strong><\/p>\n\n\n\n

The key challenge is that regulating or mandating transparency is more complex than it appears, with many elements that need to be thought through. What forms of transparency are necessary and most likely to be effective for protecting and promoting access to high-quality, diverse and independent news? What does transparency mean to policymakers, journalists, researchers and the public? How can we enable algorithmic transparency in ways that protect against political and commercial manipulation or abuse and ensure user privacy?<\/strong> What are its limitations? <\/strong>What systems or entities should be beholden to these levels of transparency? And <\/strong>who<\/em><\/strong> should be empowered to set these rules? <\/strong><\/p>\n\n\n\n

Addressing the questions introduced in this primer is a critical first step toward developing approaches to algorithmic accountability<\/a> that support an independent, competitive press and an informed society. Policymakers and other stakeholders can benefit from a more nuanced understanding of what transparency can, and cannot, accomplish as well as what new risks could be introduced. Policymakers must be forward-thinking about how and where algorithms may be used in the future, what data could be exposed and who could ultimately gain access to it as a result of increased transparency.<\/p>\n\n\n\n

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What Makes It Complex<\/h2>\n\n\n\n

One hurdle in creating valuable algorithmic transparency is being sure that those on the receiving end are armed with the necessary knowledge to assess these processes. <\/strong>A good deal of tension has arisen over how technology companies have responded to requests for independent<\/a> researcher<\/a>, journalist<\/a> and public<\/a> access<\/a> to algorithmic processes or their digital trace data<\/a>. Transparency policies must take into account each stakeholder\u2019s differing needs, aims and interests and consider what forms of transparency are necessary and appropriate for each. Because the algorithmic processes are complicated and often rely on technological jargon<\/a>, there is the risk that even with increased transparency, a lack of understanding of these processes will endure for some stakeholders.<\/p>\n\n\n\n

\u201cOpen-source\u201d transparency is not enough<\/a> to achieve an understanding of algorithmic processes or solve<\/a> the problems of platform accountability. This is true in two different manners. First, some tech leaders have suggested \u201copen-source<\/a>\u201d algorithms as an answer to transparency and accountability issues, but algorithmic processes require a level of technical knowledge most stakeholders do not possess in order to understand their complexities. \u201cOpen-source\u201d transparency requires individuals to have the time and capacity to learn the complexities of data governance and content moderation. Context is critical in learning how algorithms work; without a clear understanding of the internal policies dictating algorithmic behaviors or access to their underlying training data, raw information is not interpretable.<\/p>\n\n\n\n

Determining the parameters of transparency is challenging to do in a single policy.<\/strong> There is broad agreement that transparency<\/a> is a critical component of digital platform governance and content moderation, and that \u201cmore transparency is better,<\/a>\u201d but, it is not always clear what, and where<\/a>, transparency is being sought. Collaboration between experts is critical to addressing the multifaceted impacts policy on transparency may have. Further, the aim of transparency differs<\/a> in market-based versus state-led models, with the former aiming to empower users and the latter aiming to empower regulators. Policymakers, researchers, the news media and civil society must work together to weigh stakeholders\u2019 needs against platforms\u2019 capabilities and consider what regulatory structures will serve to enforce<\/a> this legislation. Finally, as we address in a separate issue primer<\/a>, addressing transparency on its own does not address disagreements over how to ensure people see fact-based, independent news content on digital platforms, nor does it address the choices individuals themselves make about what they click on or what kind of information they seek out.<\/p>\n\n\n\n

Digital platform regulation needs to protect against political and commercial manipulation as a consequence of algorithmic transparency.<\/strong> As discussed in our issue primer on addressing disinformation<\/a>, algorithmic transparency (and researcher access to digital trace data) can both help experts<\/a> understand the spread<\/a> of online disinformation and how to counter it and, at the same time, introduce new risks for manipulation<\/a> and abuse<\/a>. Governments in many parts of the world increasingly pressure<\/a> platforms to make content<\/a> decisions<\/a> that benefit their own political interests. There is also the risk that commercial actors or institutions manipulate algorithms to exploit virality, amplifying large quantities of clickbait and problematic content over fact-based, independent information \u2013 particularly by using new technologies<\/a> like generative<\/a> AI. Both introduce threats to an independent press and an informed public. Policy must safeguard<\/a> against these risks and insulate regulation from political and commercial manipulation, both by ensuring oversight remains independent from governments and by separating regulatory bodies from direct involvement in content moderation decisions.<\/p>\n\n\n\n

Platforms\u2019 commercial structures are real factors to be considered, both in the way they can negatively drive internal decisions and in the value they bring to our digital environment.<\/strong> Technology companies are private<\/a> business entities that operate in a competitive marketplace with a certain level of value placed on their proprietary<\/a> information and intellectual property. The current concealed nature of algorithmic selection and ranking, however, risks commercial incentives negatively affecting the work of journalists and the content the public receives. Crafting the best transparency policies will require a balance between these two elements, including consideration of potential unintended consequences for innovation or competition, such as deterring startups or smaller players from participation due to costs associated with adhering to regulation.<\/p>\n\n\n\n

Debates around transparency introduce important opportunities and risks<\/a> for the relationships between governments and platforms. <\/strong>This is particularly important when it comes to who<\/em> oversees<\/a> or regulates algorithmic transparency and what can be asked of platforms when it comes to content moderation. Legislation that does not address these critical questions can threaten an independent press and freedom of expression (for instance, via \u201cjawboning<\/a>\u201d practices<\/a> or other legal<\/a> demands of platforms). This is a consistent complexity across many of CNTI\u2019s issue areas and speaks to the importance of considering the balance between legislative and organizational policy (including government-enforced<\/em> organizational policy) in addressing these challenges.<\/p>\n\n\n\n

Policy must balance transparency and accountability against user privacy concerns when applicable. <\/strong>Digital platforms\u2019 efforts to improve transparency, when they exist at all, are at times negotiated within the contexts of user privacy concerns and data security concerns. While this does not affect all approaches to algorithmic transparency, questions about what user data digital platforms would be obligated to share and what personal information would be contained in that data will inevitably lead to trade-offs<\/a> between digital platform transparency and user privacy.<\/p>\n\n\n\n

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State of Research<\/h2>\n\n\n\n

Over the past decade, questions surrounding digital platform<\/a> governance<\/a> have moved to the forefront of political, legal and academic debates. Recently, a breadth of interdisciplinary research has focused on social media platforms and messaging apps central to global journalistic work as well as on the broader range of software companies and sharing<\/a> or gig<\/a> economy apps central to contemporary online commerce. <\/p>\n\n\n\n

Much of this research fits into two categories: governance of<\/em><\/a> social media and governance by<\/em><\/a> social media. Research focused specifically on digital platform algorithmic transparency represents one small segment of work in this field. <\/p>\n\n\n\n

To date, this research has been far more theoretical<\/a> than empirical, largely due to a lack of data (as we note in greater detail below) and the slow-moving nature of legislative policy, but it still sheds light on the varying forms<\/a> of transparency different stakeholders expect<\/a>. For instance, public<\/em><\/a> transparency via disclosures looks different from, and accomplishes different aims than, research<\/em> transparency via data access. <\/p>\n\n\n\n

Research findings have also noted the limitations<\/a> of algorithmic transparency alone as a means of establishing digital platform accountability as well as the challenges various legislative<\/a> efforts<\/a> face in addressing algorithmic transparency, ranging from potential free expression violations to individual privacy infringements. These limitations have led some experts to call for forms of digital platform accountability beyond<\/em><\/a> algorithmic transparency. <\/p>\n\n\n\n

Collectively, this work also reveals how little has changed, both via research and policy, in almost a decade of calls for algorithmic accountability.<\/p>\n\n\n\n

Looking forward, there is much that we do not yet know about digital platforms\u2019 algorithmic infrastructures. Research in this area is critical to inform current debates about internet governance. Experts have called<\/a> for independent<\/a> researchers<\/a> and civil society to be granted more access<\/a> to the inner workings of digital platform technology companies, the algorithms they develop and the trace data they collect \u2013 particularly as digital platforms steadily move away from more open<\/a> models<\/a> of free data sharing through their APIs<\/a>. Of course, what access to give to whom is itself part of the debate about transparency. Still, access, when it has the appropriate safeguards, can<\/em> help to better inform policy-making and contribute to democratic processes as a form of checks and balances on government and corporate power.<\/p>\n\n\n\n

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Notable Studies<\/h3>\n\n\n\n
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