On the Maximal Local Disparity of Fairness-Aware Classifiers

Existing group fairness notions require algorithms to treat Fairness has become a crucial aspect in the development different groups equally, and the degree of fairness violation of trustworthy machine learning algorithms. is usually measured via the dissimilarity of model Current fairness metrics to measure predictions. For example, Demographic Parity (DP) requires the violation of demographic parity have the following model predictions to be independent of sensitive attributes drawbacks: (i) the average difference of (Dwork et al., 2012; Kamishima et al., 2012; Jiang model predictions on two groups cannot reflect et al., 2020). To measure the violation of DP, most of existing their distribution disparity, and (ii) the overall calculation works adopt DP metric, which calculates the difference along all possible predictions conceals in average predictions between the two demographic the extreme local disparity at or around certain groups (Zemel et al., 2013; Chuang & Mroueh, 2021; Li predictions. In this work, we propose a novel et al., 2023b). However, since having the same values in fairness metric called Maximal Cumulative ratio average predictions between the two groups cannot ensure Disparity along varying Predictions' neighborhood that the distributions are also the same, we argue that the (MCDP), for measuring the maximal local widely used DP may fail to detect the violation of demographic disparity of the fairness-aware classifiers.