Regulating algorithmic filtering on social media

Sarah Cen, Devavrat Shah

Keywords: social media, regulation, audit, filtering algorithm, performance cost, content diversity, counterfactual, hypothesis testing, minimum-variance unbiased estimator

Abstract: By filtering the content that users see, social media platforms have the ability to influence users' perceptions and decisions, from their dining choices to their voting preferences. This influence has drawn scrutiny, with many calling for regulations on filtering algorithms, but designing and enforcing regulations remains challenging. In this work, we examine three questions. First, given a regulation, how would one design an audit to enforce it? Second, does the audit impose a performance cost on the platform? Third, how does the audit affect the content that the platform is incentivized to filter? In response to these questions, we propose a method such that, given a regulation, an auditor can test whether that regulation is met with only black-box access to the filtering algorithm. We then turn to the platform's perspective. The platform's goal is to maximize an objective function while meeting regulation. We find that there are conditions under which the regulation does not place a high performance cost on the platform and, notably, that content diversity can play a key role in aligning the interests of the platform and regulators.

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TL;DR: We propose an auditing procedure for enforcing social media regulations, provide theoretical guarantees on the audit, study whether there is a performance-regulation tradeoff, and find that content diversity plays a key role.