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Penalizing Unfairness in Binary Classification

Bechavod, Yahav and Ligett, Katrina (2017) Penalizing Unfairness in Binary Classification. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190627-153828844

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Abstract

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1707.00044arXivDiscussion Paper
ORCID:
AuthorORCID
Ligett, Katrina0000-0003-2780-6656
Additional Information:This work was supported in part by NSF grants CNS-1254169 and CNS-1518941, US-Israel Binational Science Foundation grant 2012348, Israeli Science Foundation (ISF) grant #1044/16, a subcontract on the DARPA Brandeis Project, and the HUJI Cyber Security Research Center in conjunction with the Israel National Cyber Bureau in the Prime Minister’s Office.
Funders:
Funding AgencyGrant Number
NSFCNS-1254169
NSFCNS-1518941
Binational Science Foundation (USA-Israel)2012348
Israel Science Foundation1044/16
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Hebrew University of JerusalemUNSPECIFIED
Israel National Cyber BureauUNSPECIFIED
Record Number:CaltechAUTHORS:20190627-153828844
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190627-153828844
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96801
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 Jun 2019 22:53
Last Modified:27 Jun 2019 22:53

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