Meritocratic fairness for cross-population selection

Michael Kearns, Aaron Roth, Zhiwei Steven Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We consider the problem of selecting a pool of individuals from several populations with incomparable skills (e.g. soccer players, mathematicians, and singers) in a fair manner. The quality of an individual is defined to be their relative rank (by cumulative distribution value) within their own population, which permits cross-population comparisons. We study algorithms which attempt to select the highest quality subset despite the fact that true CDF values are not known, and can only be estimated from the finite pool of candidates. Specifically, we quantify the regret in quality imposed by "meritocratic" notions of fairness, which require that individuals are selected with probability that is monotonically increasing in their true quality. We give algorithms with provable fairness and regret guarantees, as well as lower bounds, and provide empirical results which suggest that our algorithms perform better than the theory suggests.

Original languageEnglish (US)
Title of host publication34th International Conference on Machine Learning, ICML 2017
PublisherInternational Machine Learning Society (IMLS)
Pages2905-2913
Number of pages9
ISBN (Electronic)9781510855144
StatePublished - 2017
Event34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia
Duration: Aug 6 2017Aug 11 2017

Publication series

Name34th International Conference on Machine Learning, ICML 2017
Volume4

Other

Other34th International Conference on Machine Learning, ICML 2017
Country/TerritoryAustralia
CitySydney
Period8/6/178/11/17

Bibliographical note

Publisher Copyright:
© Copyright 2017 by the author(s).

Fingerprint

Dive into the research topics of 'Meritocratic fairness for cross-population selection'. Together they form a unique fingerprint.

Cite this