License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2023.4
URN: urn:nbn:de:0030-drops-179250
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17925/
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Mossé, Milan

Multiplicative Metric Fairness Under Composition

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LIPIcs-FORC-2023-4.pdf (0.6 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{mosse:LIPIcs.FORC.2023.4,
  author =	{Moss\'{e}, Milan},
  title =	{{Multiplicative Metric Fairness Under Composition}},
  booktitle =	{4th Symposium on Foundations of Responsible Computing (FORC 2023)},
  pages =	{4:1--4:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-272-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{256},
  editor =	{Talwar, Kunal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17925},
  URN =		{urn:nbn:de:0030-drops-179250},
  doi =		{10.4230/LIPIcs.FORC.2023.4},
  annote =	{Keywords: algorithmic fairness, metric fairness, fairness under composition}
}

Keywords: algorithmic fairness, metric fairness, fairness under composition
Collection: 4th Symposium on Foundations of Responsible Computing (FORC 2023)
Issue Date: 2023
Date of publication: 04.06.2023

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