A User-Driven Framework for Regulating and Auditing Social Media

• URL: http://arxiv.org/abs/2304.10525v1
• Date: Thu, 20 Apr 2023 17:53:34 GMT
• Title: A User-Driven Framework for Regulating and Auditing Social Media
• Authors: Sarah H. Cen, Aleksander Madry, Devavrat Shah
• Abstract summary: We propose that algorithmic filtering should be regulated with respect to a flexible, user-driven baseline. We require that the feeds a platform filters contain "similar" informational content as their respective baseline feeds. We present an auditing procedure that checks whether a platform honors this requirement.
• Score: 94.70018274127231
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: People form judgments and make decisions based on the information that they observe. A growing portion of that information is not only provided, but carefully curated by social media platforms. Although lawmakers largely agree that platforms should not operate without any oversight, there is little consensus on how to regulate social media. There is consensus, however, that creating a strict, global standard of "acceptable" content is untenable (e.g., in the US, it is incompatible with Section 230 of the Communications Decency Act and the First Amendment). In this work, we propose that algorithmic filtering should be regulated with respect to a flexible, user-driven baseline. We provide a concrete framework for regulating and auditing a social media platform according to such a baseline. In particular, we introduce the notion of a baseline feed: the content that a user would see without filtering (e.g., on Twitter, this could be the chronological timeline). We require that the feeds a platform filters contain "similar" informational content as their respective baseline feeds, and we design a principled way to measure similarity. This approach is motivated by related suggestions that regulations should increase user agency. We present an auditing procedure that checks whether a platform honors this requirement. Notably, the audit needs only black-box access to a platform's filtering algorithm, and it does not access or infer private user information. We provide theoretical guarantees on the strength of the audit. We further show that requiring closeness between filtered and baseline feeds does not impose a large performance cost, nor does it create echo chambers.

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