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Most works on the fairness of machine learning systems focus on the blindoptimization of common fairness metrics, such as Demographic Parity andEqualized Odds. In this paper, we conduct a comparative study of several biasmitigation approaches to investigate their behaviors at a fine grain, theprediction level. Our objective is to characterize the differences between fairmodels obtained with different approaches. With comparable performances infairness and accuracy, are the different bias mitigation approaches impacting asimilar number of individuals? Do they mitigate bias in a similar way? Do theyaffect the same individuals when debiasing a model? Our findings show that biasmitigation approaches differ a lot in their strategies, both in the number ofimpacted individuals and the populations targeted. More surprisingly, we showthese results even apply for several runs of the same mitigation approach.These findings raise questions about the limitations of the current groupfairness metrics, as well as the arbitrariness, hence unfairness, of the wholedebiasing process.

When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms