Data Bias Mitigation under Coverage Constraints & The Price of Fairness

2026-06-18

AI Summary

This work addresses the persistent underperformance of machine learning models on intersecting sensitive subgroups—such as those defined by race and gender—attributed to inadequate bias metrics and insufficient representation in training data. The authors introduce coverage constraints into a bias mitigation framework for the first time, formulating the problem via integer linear programming to optimize data modification costs while ensuring adequate representation across all (intersecting) groups and bounding approximation error in bias reduction. This approach enables quantification of the “price of fairness,” facilitating principled trade-offs between equity and data efficiency in legal compliance and data governance contexts. Empirical evaluations across multiple benchmark datasets and classifiers demonstrate that the proposed framework effectively preserves predictive accuracy while substantiating the critical role of coverage constraints in safeguarding both fairness and performance in downstream models.

Abstract

Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender. This stems in part from two interrelated challenges: the lack of principled measures for quantifying bias (potentially intersectional), and insufficient representation of intersectional subgroups in training data. We extend a recent bias mitigation framework to incorporate coverage constraints that enforce sufficient representation across groups, including intersectional subgroups. Since achieving exactly zero bias for all groups may not be data efficient (meaning it may require large amounts of data), our solution trades small approximation errors in bias for greater data efficiency while satisfying coverage constraints. We also formulate bias mitigation as an integer linear program that optimizes over all mitigation strategies, and characterize the price of fairness, the minimum data modification cost, as a function of fairness tolerance. This is essential both for legal compliance, where regulations may mandate specific fairness thresholds, and for data governance, enabling practitioners to make informed trade-offs between bias reduction and data modification (particularly, data purchasing) costs. We evaluate our techniques on publicly available datasets, demonstrating that bias mitigation via our framework preserves predictive accuracy across multiple classifiers, and that coverage constraints, while motivated by statistical considerations, are essential for preserving downstream ML performance.

Problem

• data bias
• intersectionality
• coverage constraints
• fairness
• representation

Innovation

• coverage constraints
• intersectional fairness
• price of fairness
• bias mitigation
• integer linear programming