Fairness-aware Regression Robust to Adversarial Attacks

• URL: http://arxiv.org/abs/2211.04449v1
• Date: Fri, 4 Nov 2022 18:09:34 GMT
• Title: Fairness-aware Regression Robust to Adversarial Attacks
• Authors: Yulu Jin and Lifeng Lai
• Abstract summary: We take a first step towards answering the question of how to design fair machine learning algorithms that are robust to adversarial attacks. For both synthetic data and real-world datasets, numerical results illustrate that the proposed adversarially robust models have better performance on poisoned datasets than other fair machine learning models.
• Score: 46.01773881604595
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we take a first step towards answering the question of how to design fair machine learning algorithms that are robust to adversarial attacks. Using a minimax framework, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to add a carefully designed adversarial data point to the dataset or perform a rank-one attack on the dataset. By solving the proposed nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well as the robust fairness-aware regression model are obtained. For both synthetic data and real-world datasets, numerical results illustrate that the proposed adversarially robust fair models have better performance on poisoned datasets than other fair machine learning models in both prediction accuracy and group-based fairness measure.

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