{"id":35414,"date":"2022-11-16T15:12:14","date_gmt":"2022-11-16T15:12:14","guid":{"rendered":"https:\/\/publicknowledge.org\/?p=35414"},"modified":"2022-11-16T15:12:15","modified_gmt":"2022-11-16T15:12:15","slug":"should-algorithms-be-regulated-part-3-evaluating-alternative-frameworks-for-regulating-algorithms","status":"publish","type":"post","link":"https:\/\/publicknowledge.org\/should-algorithms-be-regulated-part-3-evaluating-alternative-frameworks-for-regulating-algorithms\/","title":{"rendered":"Should Algorithms Be Regulated? Part 3: Evaluating Alternative Frameworks for Regulating Algorithms"},"content":{"rendered":"\n

This is the third in a series of blog posts from Public Knowledge examining the public policy implications of algorithmic decision-making. A first post<\/a> clarified what algorithms are and aren\u2019t and identified some basic principles for attempting to regulate algorithmic decision-making, especially as it relates to content distribution. The second post<\/a> cataloged the harms that can arise from algorithmic decision-making, including (1) harms to safety and well-being; (2) harms to economic justice; and (3) harms to democratic participation. (We\u2019ve also written in other forums<\/a> about the harmful consequences of unregulated, unproven AI.) <\/p>\n\n\n\n

In this post and the accompanying policy framework<\/a>, we seek to assess various theories of change and related policy frameworks for creating accountability for algorithmic decision-making. We describe each approach\u2019s drawbacks and benefits and put forward Public Knowledge\u2019s perspective.\u00a0<\/p>\n\n\n\n

Spoiler alert: There is no single or simple solution for the complex questions our exploration has raised. As you\u2019ll see in our framework<\/a>, there are several potential approaches for trying to mitigate the myriad harms algorithmic decision-making about content can create through direct regulation of algorithms. But most of them face substantial hurdles, particularly those that entail or even approach<\/em> the regulation of user-generated content \u2013 most of which is protected speech. The hurdles include constitutional challenges, patterns of Supreme Court jurisprudence, or narrow or ambiguous definitions<\/a> that would compound the challenges of content moderation for both platforms and users. As a Federal Trade Commission report<\/a> on combating online harms noted, \u201cgovernments, platforms, and others must exercise great caution\u201d and focus attention on a broad array of considerations before turning to regulation to mitigate online harms.\u00a0<\/p>\n\n\n\n

The challenge of creating accountability for the harms associated with algorithmic decision-making is not for lack of effort on the part of advocates or policymakers. In the last year or so, accelerated by whistleblower revelations,<\/a> many bills were introduced that target the advertising-based business model that motivates platforms to use algorithms to distribute content based primarily on a profit motive rather than the public interest. Academics, legal scholars, civil society groups, and others have assessed these proposals exhaustively and brought forward some of their own. We feel our greatest opportunity to add value to this discourse is not to duplicate that effort but to summarize it and present it in a way that can serve as an aid to policymakers. <\/p>\n\n\n\n

We evaluated eight alternative policy frameworks that have been proposed \u2013 usually in the form of actual legislation \u2013 for regulating algorithmic decision-making. Each one is predicated on a distinct theory of change. These policy frameworks include:<\/p>\n\n\n\n

  1. Increasing platform accountability for certain forms of content.\u00a0<\/li>
  2. Increasing platform accountability for amplifying any <\/em>form of content via complex algorithms.<\/li>
  3. Introducing platform requirements for transparency, choice, and due process in algorithmic content moderation.<\/li>
  4. Introducing privacy regimes that disincentivize or outright ban certain forms of data collection and use.\u00a0<\/li>
  5. Extending product safety regimes into algorithmic product design.<\/li>
  6. Implementing a dedicated digital regulator with broad jurisdiction, strong enforcement, and rulemaking authority.<\/li>
  7. Increasing platform accountability for the platforms\u2019 ad-based business model.\u00a0<\/li>
  8. Increasing the power of the user by expanding antitrust and competition policy.<\/li><\/ol>\n\n\n\n

    After an evaluation of what proponents and opponents of each framework have to say, and evaluating each of the bills that embody each framework, we conclude that the optimal solution to mitigating the harms of algorithmic decision-making about content distribution entails a combination of strategies:<\/p>\n\n\n\n