Characterizing the risk of fairwashing

TL;DR

This paper investigates the potential for fairwashing attacks to misrepresent unfair models as fair through explanation manipulation, highlighting their transferability, generalization, and detection challenges.

Contribution

It analyzes the fidelity-unfairness trade-offs of fairwashing, demonstrating their transferability and proposing a method to quantify fairwashing risk.

Findings

Fairwashed explainers can generalize beyond the explained data points.

Fairwashing attacks can transfer across different black-box models.

Proposed approach quantifies fairwashing risk via unfairness range of high-fidelity explainers.

Abstract

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.