Training individually fair ML models with Sensitive Subspace Robustness

Mikhail Yurochkin; Amanda Bower; Yuekai Sun

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arXiv · 1907.00020

Training individually fair ML models with Sensitive Subspace Robustness

Mikhail Yurochkin·Amanda Bower·Yuekai Sun

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Abstract

We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases.

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Mikhail Yurochkin, Amanda Bower, Yuekai Sun. 2019-06-28. Training individually fair ML models with Sensitive Subspace Robustness. https://arxiv.org/abs/1907.00020

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