Dec 6, 2021
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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. In this work, we examine two questions. First, how does the filtered content affect a user's learning and decision-making and is there a principled way to regulate this effect? Second, does regulating algorithmic filtering necessarily impose a large performance cost on the platform? We approach these questions by studying a system with three actors: a platform, a user, and an auditor. 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 R while meeting regulation. We find that there are conditions under which the cost of regulation is low and, interestingly, content diversity plays a key role in the platform's ability to achieve high R while meeting regulation.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. In this work, we examine two questions. First, how does the filtered content affect a user's learning and decision-making and is there a principled way to regulate this effect? Second, does regulating algorithmic filtering…
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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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