  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.
