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：建议者系统由于对我们获得的机会的影响越来越大而面临审查。目前对公平性的审计仅限于敏感群体一级粗略的对等评估。我们提议对无妒忌性进行审计,这是与个人偏好一致的更为细微的标准:每个用户应偏向于他们的建议而不是其他用户的建议。由于对嫉妒性的审计需要估计用户的偏好,而不是现有建议,我们把审计看成是多武装匪徒中一个新的纯粹的探索问题。我们提出了具有理论保证的抽样高效算法,其理论保证不会恶化用户的经验。我们还研究了在现实世界建议数据集中实现的权衡。