Understanding and Addressing Fairwashing in Machine Learning by Sébastien Gambs

Описание: 22nd Annual International Conference on Privacy, Security & Trust, August 2025.

Dr. Sébastien Gambs, Canada Research Chair in Privacy-Preserving & Ethical Analysis of Big Data, UQAM

Fairwashing refers to the risk that an unfair black-box model can be explained by a fairer model through post-hoc explanation manipulation. In this talk, I will first discuss how fairwashing attacks can transfer across black-box models, meaning that other black-box models can perform fairwashing without explicitly using their predictions. This generalization and transferability of fairwashing attacks imply that their detection will be difficult in practice. Finally, I will nonetheless review some possible avenues of research on how to limit the potential for fairwashing.

Sébastien Gambs has held the Canada Research Chair in Privacy and Ethical Analysis of Massive Data since December 2017 and has been a professor in the Department of Computer Science at the Université du Québec à Montréal since January 2016. His main research theme is privacy in the digital world. He is also interested in solving long-term scientific questions such as the existing tensions between massive data analysis and privacy as well as ethical issues such as fairness, transparency and algorithmic accountability raised by personalized systems.
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