We propose to assess the fairness of personalized recommender systems in the sense of envy-freeness: every (group of) user(s) should prefer their recommendations to the recommendations of other (groups of) users. Auditing for envy-freeness requires probing user preferences to detect potential blind spots, which may deteriorate recommendation performance. To control the cost of exploration, we propose an auditing algorithm based on pure exploration and conservative constraints in multi-armed bandits. We study, both theoretically and empirically, the trade-offs achieved by this algorithm.

翻译：我们提议评估个性化推荐人制度的公平性,从无妒忌的意义上说:每个(群体)用户应该选择他们的建议,而不是其他(群体)用户的建议。对无妒忌的审计要求调查用户的偏好,以发现潜在的盲点,这可能会降低建议性能。为了控制勘探成本,我们建议基于纯探索和对多武装强盗保守限制的审计算法。我们从理论上和经验上研究这一算法的权衡。