2026-07-21T21:12:25Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:12019 2024-03-06T10:49:06Z ddc:004 open_access

Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? Blum, Avrim Stangl, Kevin fairness in machine learning equal opportunity bias machine learning 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Avrim Blum and Kevin Stangl 2020 Is Part Of LIPIcs, Volume 156, 1st Symposium on Foundations of Responsible Computing (FORC 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2020.3 urn:nbn:de:0030-drops-120192 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.3 eng https://creativecommons.org/licenses/by/3.0/legalcode