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Gerrymandering individual fairness.

Authors :
Räz, Tim
Source :
Artificial Intelligence. Jan2024, Vol. 326, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

Individual fairness requires that similar individuals are treated similarly. It is supposed to prevent the unfair treatment of individuals on the subgroup level and to overcome the problem that group fairness measures are susceptible to manipulation or gerrymandering. The goal of the present paper is to explore the extent to which individual fairness itself can be gerrymandered. It will be proved that individual fairness can be gerrymandered in the context of predicting scores. Then, it will be argued that individual fairness is a very weak notion of fairness for some choices of feature space and metric. Finally, it will be discussed which properties of (individual) fairness are desirable. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00043702
Volume :
326
Database :
Academic Search Index
Journal :
Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
174030923
Full Text :
https://doi.org/10.1016/j.artint.2023.104035