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

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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.

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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

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.

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