A Geometric Theory of Robust Fairness Audits

Abstract:Neighborhood-based fairness audits evaluate individual fairness by comparing predictions among similar individuals in feature space. Despite their widespread use, little is known about the robustness of the auditing procedure itself. Because these audits rely on nearest neighbor relationships, small perturbations in feature space can alter local neighborhoods and produce different fairness assessments even when model predictions remain unchanged. We develop a geometric framework for analyzing the robustness of neighborhood-based fairness audits under bounded perturbations. Our analysis establishes sufficient conditions for neighborhood invariance, quantifies how neighborhood replacement propagates to audit instability, and introduces audit volatility, a measure of the expected sensitivity of fairness audits under repeated perturbations. Experiments on benchmark datasets support the theoretical analysis and show that the proposed framework explains the observed stability of neighborhood-based fairness audits.

Machine Learning (cs.LG)

arXiv:2608.24818 [cs.LG]

(or arXiv:2608.24818v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.24818 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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