Characterizing the risk of fairwashing
This addresses the problem of deceptive fairness in AI for stakeholders relying on model explanations, but it is incremental as it builds on existing fairwashing concepts.
The paper investigates fairwashing attacks, showing that manipulated explanations can generalize beyond the original data and transfer across models, making detection difficult, and proposes a method to quantify this risk based on unfairness ranges of high-fidelity explainers.
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.