To be Robust and to be Fair: Aligning Fairness with Robustness

TL;DR

This paper investigates the relationship between fairness and robustness in adversarial training, proposing a unified approach that improves model performance on both metrics through theoretical insights and experimental validation.

Contribution

It introduces a unified fairness attack framework, proves the equivalence of different fairness notions, and demonstrates that robustness in one metric benefits the other.

Findings

Unified fairness attack framework established

Theoretical proof of equivalence among fairness notions

Improved robustness on both fairness and accuracy metrics

Abstract

Adversarial training has been shown to be reliable in improving robustness against adversarial samples. However, the problem of adversarial training in terms of fairness has not yet been properly studied, and the relationship between fairness and accuracy attack still remains unclear. Can we simultaneously improve robustness w.r.t. both fairness and accuracy? To tackle this topic, in this paper, we study the problem of adversarial training and adversarial attack w.r.t. both metrics. We propose a unified structure for fairness attack which brings together common notions in group fairness, and we theoretically prove the equivalence of fairness attack against different notions. Moreover, we show the alignment of fairness and accuracy attack, and theoretically demonstrate that robustness w.r.t. one metric benefits from robustness w.r.t. the other metric. Our study suggests a novel way to unify adversarial training and attack w.r.t. fairness and accuracy, and experimental results show that our proposed method achieves better performance in terms of robustness w.r.t. both metrics.