Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
arxiv(2024)
摘要
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.
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