License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2020.3
URN: urn:nbn:de:0030-drops-120192
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/12019/
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Blum, Avrim ; Stangl, Kevin

Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?

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LIPIcs-FORC-2020-3.pdf (0.6 MB)

Abstract

Multiple fairness constraints have been proposed in the literature, motivated by a range of concerns about how demographic groups might be treated unfairly by machine learning classifiers. In this work we consider a different motivation; learning from biased training data. We posit several ways in which training data may be biased, including having a more noisy or negatively biased labeling process on members of a disadvantaged group, or a decreased prevalence of positive or negative examples from the disadvantaged group, or both. Given such biased training data, Empirical Risk Minimization (ERM) may produce a classifier that not only is biased but also has suboptimal accuracy on the true data distribution. We examine the ability of fairness-constrained ERM to correct this problem. In particular, we find that the Equal Opportunity fairness constraint [Hardt et al., 2016] combined with ERM will provably recover the Bayes optimal classifier under a range of bias models. We also consider other recovery methods including re-weighting the training data, Equalized Odds, and Demographic Parity, and Calibration. These theoretical results provide additional motivation for considering fairness interventions even if an actor cares primarily about accuracy.

BibTeX - Entry

@InProceedings{blum_et_al:LIPIcs:2020:12019,
  author =	{Avrim Blum and Kevin Stangl},
  title =	{{Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?}},
  booktitle =	{1st Symposium on Foundations of Responsible Computing (FORC 2020)},
  pages =	{3:1--3:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-142-9},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{156},
  editor =	{Aaron Roth},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12019},
  URN =		{urn:nbn:de:0030-drops-120192},
  doi =		{10.4230/LIPIcs.FORC.2020.3},
  annote =	{Keywords: fairness in machine learning, equal opportunity, bias, machine learning}
}

Keywords: fairness in machine learning, equal opportunity, bias, machine learning
Collection: 1st Symposium on Foundations of Responsible Computing (FORC 2020)
Issue Date: 2020
Date of publication: 18.05.2020

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