Characterizing the risk of fairwashing - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Characterizing the risk of fairwashing

Résumé

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 paper, we investigate the capability of fairwashing attacks by analyzing their fidelity-unfairness trade-offs. In particular, we show that fairwashed explanation models can generalize beyond the suing group (i.e., data points that are being explained), meaning that a fairwashed explainer can be used to rationalize subsequent unfair decisions of a black-box model. We also demonstrate that 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, we propose an approach to quantify the risk of fairwashing, which is based on the computation of the range of the unfairness of high-fidelity explainers.

Dates et versions

hal-04255132 , version 1 (23-10-2023)

Identifiants

Citer

Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs, Satoshi Hara. Characterizing the risk of fairwashing. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Dec 2022, New Orleans (Louisiana), United States. pp.14822--14834, ⟨10.48550/arXiv.2106.07504⟩. ⟨hal-04255132⟩
25 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More