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.

翻译：人们基于观察到的信息形成判断并做出决策。而社交平台不仅提供了这些信息中日益增长的部分，还对其进行了精心筛选。尽管立法者普遍认为平台不应在毫无监管的情况下运作，但关于如何规范社交媒体却鲜有共识。不过，各方一致认为，制定一条严格的、全球统一的"可接受"内容标准是行不通的（例如在美国，这与《通信规范法》第230条及第一修正案相悖）。本研究提出，算法过滤的监管应建立在灵活、用户驱动的基准之上。我们据此提供了具体框架来规范并审计社交媒体平台。具体而言，我们引入"基准信息流"概念：即用户未经过滤时能看到的内容（例如在推特上，可能按时间顺序排列的信息流）。我们要求平台过滤后的信息流必须包含与各自基准信息流"相似"的信息内容，并设计了原则性的相似度衡量方法。该思路源于相关建议——监管应增强用户自主权。我们提出了一种审计程序，用以核查平台是否遵守此要求。值得注意的是，该审计仅需对平台的过滤算法进行黑盒访问，无需获取或推断用户私人信息。我们为审计的效力提供了理论保障，并进一步证明：要求过滤信息流与基准信息流保持近似性，既不会造成显著的性能损失，也不会催生"信息茧房"。