  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.
