Preventing fairness gerrymandering: Auditing and learning for subgroup fairness

Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

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

34 Scopus citations

Abstract

We introduce a new family of fairness definitions that interpolate between statistical and individual notions of fairness, obtaining some of the best properties of each. We show that checking whether these notions are satisfied is computationally hard in the worst case, but give practical oracle-efficient algorithms for learning subject to these constraints, and confirm our findings with experiments.

Original languageEnglish (US)
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
PublisherInternational Machine Learning Society (IMLS)
Pages4008-4016
Number of pages9
ISBN (Electronic)9781510867963
StatePublished - Jan 1 2018
Externally publishedYes
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: Jul 10 2018Jul 15 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume6

Other

Other35th International Conference on Machine Learning, ICML 2018
CountrySweden
CityStockholm
Period7/10/187/15/18

Fingerprint Dive into the research topics of 'Preventing fairness gerrymandering: Auditing and learning for subgroup fairness'. Together they form a unique fingerprint.

  • Cite this

    Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2018). Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In J. Dy, & A. Krause (Eds.), 35th International Conference on Machine Learning, ICML 2018 (pp. 4008-4016). (35th International Conference on Machine Learning, ICML 2018; Vol. 6). International Machine Learning Society (IMLS).