SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness

2020/06/25 by Mikhail Yurochkin, Yuekai Sun, Yurochkin, Mikhail +1 · 4 citations
Social Sciences · Computer Science · #Ethics and Social Impacts of AI #Adversarial Robustness in Machine Learning #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2006.14168

Abstract

In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regularizer efficiently. Our theoretical results guarantee the proposed approach trains certifiably fair ML models. Finally, in the experimental studies we demonstrate improved fairness metrics in comparison to several recent fair training procedures on three ML tasks that are susceptible to algorithmic bias.

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