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We evaluated whether model explanations could efficiently detect bias inimage classification by highlighting discriminating features, thereby removingthe reliance on sensitive attributes for fairness calculations. To this end, weformulated important characteristics for bias detection and observed howexplanations change as the degree of bias in models change. The paperidentifies strengths and best practices for detecting bias using explanations,as well as three main weaknesses: explanations poorly estimate the degree ofbias, could potentially introduce additional bias into the analysis, and aresometimes inefficient in terms of human effort involved.

Investigating Bias in Image Classification using Model Explanations