[Re] Exacerbating Algorithmic Bias through Fairness Attacks

Angelos Nalmpantis · Apostolos Panagiotopoulos · John Gkountouras · Konstantinos Papakostas

Hall J #1004

Keywords: [ ReScience - MLRC 2021 ] [ Journal Track ]


We conducted a reproducibility study of the paper 'Exacerbating Algorithmic Bias through Fairness Attacks'. According to the paper, current research on adversarial attacks is primarily focused on targeting model performance, which motivates the need for adversarial attacks on fairness. To that end, the authors propose two novel data poisoning adversarial attacks, the influence attack on fairness and the anchoring attack. We aim to verify the main claims of the paper, namely that: a) the proposed methods indeed affect a model's fairness and outperform existing attacks, b) the anchoring attack hardly affects performance, while impacting fairness, and c) the influence attack on fairness provides a controllable trade-off between performance and fairness degradation.

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