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：推荐系统在求职、电子商务等领域不可或缺，因此为具有不同敏感属性的用户提供公平推荐成为一项迫切需求。先前增强推荐系统公平性的方法假设所有敏感属性均可获取，但由于隐私顾虑或获取手段不足，这一假设往往难以实现。实践中，这些方法的有效性受限，促使我们探索在有限敏感属性信息下促进公平性的方法。为此，重建缺失敏感属性至关重要。然而，由于现实世界中敏感属性重建问题的复杂性及法律限制，重建误差不可避免。因此，我们寻求对重建误差具有鲁棒性的公平学习方法。基于此，我们提出分布鲁棒公平优化方法（DRFO），该方法最小化缺失敏感属性所有潜在概率分布下的最坏情况不公平性，而非仅针对重建分布，以考虑重建误差的影响。我们通过理论和实证证据表明，该方法在仅能获取有限敏感属性时，能够有效确保推荐系统的公平性。