Why is my classifier discriminatory?
Name
NeurIPS-2018-why-is-my-classifier-discriminatory-Paper.pdf
Description
Published version
Size
471.68 KB
Format
Adobe PDF
Checksum (MD5)
aeb655bf4ef779e909002c8c659be820
Author(s)
Sontag, David
Johansson, Fredrik D.
Date Issued
2018
Journal
Advances in Neural Information Processing Systems
Citation
Sontag, David and Johansson, Fredrik D. 2018. "Why is my classifier discriminatory?." Advances in Neural Information Processing Systems, 2018-December.
Version
Final published version
Abstract
© 2018 Curran Associates Inc..All rights reserved. Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2018/hash/1f1baa5b8edac74eb4eaa329f14a0361-Abstract.html
