Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach

TL;DR

This paper introduces a distributionally robust optimization method for fair recommender systems that effectively handles limited sensitive attribute data and reconstruction errors, ensuring fairness despite data limitations.

Contribution

It proposes a novel distributionally robust fair optimization approach that accounts for reconstruction errors in sensitive attributes, improving fairness in recommender systems with limited attribute data.

Findings

The method effectively promotes fairness under limited sensitive attribute information.

It demonstrates robustness to reconstruction errors through theoretical and empirical analysis.

The approach outperforms existing fairness methods in scenarios with incomplete sensitive attribute data.

Abstract

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.