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Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?

Authors Avrim Blum , Kevin Stangl

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LIPIcs.FORC.2020.3.pdf
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Author Details

Avrim Blum
  • Toyota Technological Institute at Chicago, 6045 South Kenwood Avenue, Chicago, IL, 60637, USA
Kevin Stangl
  • Toyota Technological Institute at Chicago, 6045 South Kenwood Avenue, Chicago, IL, 60637, USA

Funding

  • Blum, Avrim: Supported in part by the National Science Foundation under grants CCF-1815011 and CCF-1733556.
  • Stangl, Kevin: Supported in part by the National Science Foundation under grant CCF-1815011.

Acknowledgements

We would like to thank Jon Kleinberg and Manish Raghavan for their helpful and insightful comments on an earlier draft of this manuscript.

Cite AsGet BibTex

Avrim Blum and Kevin Stangl. Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.FORC.2020.3

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Machine learning theory
Keywords
  • fairness in machine learning
  • equal opportunity
  • bias
  • machine learning

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