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- W4308672550 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." @default.
- W4308672550 created "2022-11-14" @default.
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- W4308672550 date "2022-11-04" @default.
- W4308672550 modified "2023-09-28" @default.
- W4308672550 title "Fairness-aware Regression Robust to Adversarial Attacks" @default.
- W4308672550 doi "https://doi.org/10.48550/arxiv.2211.04449" @default.
- W4308672550 hasPublicationYear "2022" @default.
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