Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity assessments at the level of sensitive groups. We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users. Since auditing for envy requires to estimate the preferences of users beyond their existing recommendations, we cast the audit as a new pure exploration problem in multi-armed bandits. We propose a sample-efficient algorithm with theoretical guarantees that it does not deteriorate user experience. We also study the trade-offs achieved on real-world recommendation datasets.

翻译：推荐系统因其对我们所接触机会的日益增长的影响而受到审视。当前的公平性审计仅限于在敏感群体层面进行粗粒度的均等性评估。我们提出对无嫉妒性（envy-freeness）进行审计，这是一种更细粒度的、与个人偏好相契合的标准：每个用户都应偏好自己的推荐结果而非其他用户的推荐结果。由于对无嫉妒性进行审计需要估计用户超出其现有推荐结果的偏好，我们将该审计问题转化为多臂老虎机中的一个新型纯探索问题。我们提出了一种样本高效的算法，并提供了其不会降低用户体验的理论保证。我们还研究了该算法在真实世界推荐数据集上实现的权衡。