Social media platforms (SMPs) leverage algorithmic filtering (AF) as a means of selecting the content that constitutes a user's feed with the aim of maximizing their rewards. Selectively choosing the contents to be shown on the user's feed may yield a certain extent of influence, either minor or major, on the user's decision-making, compared to what it would have been under a natural/fair content selection. As we have witnessed over the past decade, algorithmic filtering can cause detrimental side effects, ranging from biasing individual decisions to shaping those of society as a whole, for example, diverting users' attention from whether to get the COVID-19 vaccine or inducing the public to choose a presidential candidate. The government's constant attempts to regulate the adverse effects of AF are often complicated, due to bureaucracy, legal affairs, and financial considerations. On the other hand SMPs seek to monitor their own algorithmic activities to avoid being fined for exceeding the allowable threshold. In this paper, we mathematically formalize this framework and utilize it to construct a data-driven statistical auditing procedure to regulate AF from deflecting users' beliefs over time, along with sample complexity guarantees. This state-of-the-art algorithm can be used either by authorities acting as external regulators or by SMPs for self-auditing.

翻译：社交媒体平台（SMP）利用算法过滤（AF）作为选择构成用户信息流内容的手段，以最大化其收益。与自然/公平的内容选择相比，选择性展示用户信息流中的内容可能对用户的决策产生一定程度的（或大或小）影响。正如我们在过去十年中所见证的，算法过滤可能导致有害的副作用，从偏向个人决策到塑造整个社会决策，例如转移用户对是否接种新冠疫苗的注意力，或诱导公众选择某位总统候选人。政府试图规范AF负面影响的努力常因官僚主义、法律事务和财务考量而变得复杂。另一方面，SMP试图监控自身的算法活动，以避免因超出允许阈值而被罚款。在本文中，我们对该框架进行了数学形式化，并利用它构建了一个基于数据的统计审计程序，配合样本复杂度保证，以规范AF随时间推移对用户信念的偏离。这一最先进的算法既可由作为外部监管机构的权力机关使用，也可由SMP用于自我审计。