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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 10: AAAI-21 Technical Tracks 10

Exacerbating Algorithmic Bias through Fairness Attacks

February 1, 2023

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Authors

Ninareh Mehrabi

University of Southern California Information Sciences Institute


Muhammad Naveed

University of Southern California


Fred Morstatter

University of Southern California Information Sciences Institute


Aram Galstyan

University of Southern California Information Sciences Institute


DOI:

10.1609/aaai.v35i10.17080


Abstract:

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been properly addressed. Indeed, most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system, without any regard to the system's fairness. We propose new types of data poisoning attacks where an adversary intentionally targets the fairness of a system. Specifically, we propose two families of attacks that target fairness measures. In the anchoring attack, we skew the decision boundary by placing poisoned points near specific target points to bias the outcome. In the influence attack on fairness, we aim to maximize the covariance between the sensitive attributes and the decision outcome and affect the fairness of the model. We conduct extensive experiments that indicate the effectiveness of our proposed attacks.

Topics: AAAI

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HOW TO CITE:

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan Exacerbating Algorithmic Bias through Fairness Attacks Proceedings of the AAAI Conference on Artificial Intelligence (2021) 8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan Exacerbating Algorithmic Bias through Fairness Attacks AAAI 2021, 8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan (2021). Exacerbating Algorithmic Bias through Fairness Attacks. Proceedings of the AAAI Conference on Artificial Intelligence, 8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan. Exacerbating Algorithmic Bias through Fairness Attacks. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan. 2021. Exacerbating Algorithmic Bias through Fairness Attacks. "Proceedings of the AAAI Conference on Artificial Intelligence". 8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan. (2021) "Exacerbating Algorithmic Bias through Fairness Attacks", Proceedings of the AAAI Conference on Artificial Intelligence, p.8930-8938

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan, "Exacerbating Algorithmic Bias through Fairness Attacks", AAAI, p.8930-8938, 2021.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan. "Exacerbating Algorithmic Bias through Fairness Attacks". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan. "Exacerbating Algorithmic Bias through Fairness Attacks". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 8930-8938.

Ninareh Mehrabi||Muhammad Naveed||Fred Morstatter||Aram Galstyan. Exacerbating Algorithmic Bias through Fairness Attacks. AAAI[Internet]. 2021[cited 2023]; 8930-8938.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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