Unified Fairness from Data to Learning Algorithm

Yanfu Zhang, Lei Luo, Heng Huang

Abstract: In classification problems, individual fairness prevents discrimination against individuals based on protected attributes. Fairness-aware methods usually consist of two stages, first determining a fair metric concerning the similarity between different instances and then learning the fairness-aware model. However, existing works usually consider these two stages separately and only focus on improving the individual stage. Moreover, the choice of fair metric is heavily dependent on the task or dataset of interest, which requires ad-hoc domain knowledge and introduces extra difficulty into algorithm designing. As such, this discrepancy presumably leads to sub-optimal fairness-aware pipelines for different applications. In this paper, we propose to fill in the fairness learning gap between these two stages by automatically learning an effective metric integrated into the fairness of both data and classifiers. Specifically, we formulate the fairness-aware classification as a distributional robustness optimization problem based on deep metric learning and propose an effective optimization algorithm to solve it. Meanwhile, we establish the asymptotically unbiased generalization bounds for the proposed algorithm using the techniques of U-statistics. The experimental results on popular benchmark datasets demonstrate that the proposed approach achieves consistent improvement concerning several fairness assessments.