As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes becomes an imperative requirement. Prior approaches for enhancing fairness in recommender systems presume the availability of all sensitive attributes, which can be difficult to obtain due to privacy concerns or inadequate means of capturing these attributes. In practice, the efficacy of these approaches is limited, pushing us to investigate ways of promoting fairness with limited sensitive attribute information. Toward this goal, it is important to reconstruct missing sensitive attributes. Nevertheless, reconstruction errors are inevitable due to the complexity of real-world sensitive attribute reconstruction problems and legal regulations. Thus, we pursue fair learning methods that are robust to reconstruction errors. To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors. We provide theoretical and empirical evidence to demonstrate that our method can effectively ensure fairness in recommender systems when only limited sensitive attributes are accessible.

翻译：随着推荐系统在求职、电子商务等领域的不可或缺性日益凸显，向具有不同敏感属性的用户提供公平的推荐已成为一项迫切需求。先前提升推荐系统公平性的方法均假定所有敏感属性均可获取，然而由于隐私顾虑或属性捕获手段不足，这些属性往往难以获得。在实践中，这些方法的有效性受到限制，这促使我们探索如何在有限敏感属性信息下促进公平性。为实现这一目标，重建缺失的敏感属性至关重要。然而，由于现实世界中敏感属性重建问题的复杂性以及法律法规的限制，重建误差不可避免。因此，我们致力于寻求对重建误差具有鲁棒性的公平学习方法。为此，我们提出了分布鲁棒公平优化（DRFO）方法，该方法通过最小化所有缺失敏感属性潜在概率分布（而非仅基于重建分布）下的最坏情况不公平性，以考量重建误差的影响。我们提供了理论与实证证据，证明当仅能获取有限敏感属性时，我们的方法能有效确保推荐系统的公平性。