eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2023-06-04
4:1
4:11
10.4230/LIPIcs.FORC.2023.4
article
Multiplicative Metric Fairness Under Composition
Mossé, Milan
1
Department of Philosophy, University of California at Berkeley, CA, USA
Dwork, Hardt, Pitassi, Reingold, & Zemel [Dwork et al., 2012] introduced two notions of fairness, each of which is meant to formalize the notion of similar treatment for similarly qualified individuals. The first of these notions, which we call additive metric fairness, has received much attention in subsequent work studying the fairness of a system composed of classifiers which are fair when considered in isolation [Chawla and Jagadeesan, 2020; Chawla et al., 2022; Dwork and Ilvento, 2018; Dwork et al., 2020; Ilvento et al., 2020] and in work studying the relationship between fair treatment of individuals and fair treatment of groups [Dwork et al., 2012; Dwork and Ilvento, 2018; Kim et al., 2018]. Here, we extend these lines of research to the second, less-studied notion, which we call multiplicative metric fairness. In particular, we exactly characterize the fairness of conjunctions and disjunctions of multiplicative metric fair classifiers, and the extent to which a classifier which satisfies multiplicative metric fairness also treats groups fairly. This characterization reveals that whereas additive metric fairness becomes easier to satisfy when probabilities of acceptance are small, leading to unfairness under functional and group compositions, multiplicative metric fairness is better-behaved, due to its scale-invariance.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol256-forc2023/LIPIcs.FORC.2023.4/LIPIcs.FORC.2023.4.pdf
algorithmic fairness
metric fairness
fairness under composition