  We evaluated whether model explanations could efficiently detect bias in
image classification by highlighting discriminating features, thereby removing
the reliance on sensitive attributes for fairness calculations. To this end, we
formulated important characteristics for bias detection and observed how
explanations change as the degree of bias in models change. The paper
identifies strengths and best practices for detecting bias using explanations,
as well as three main weaknesses: explanations poorly estimate the degree of
bias, could potentially introduce additional bias into the analysis, and are
sometimes inefficient in terms of human effort involved.
