Auditing ML Models for Individual Bias and Unfairness

Songkai Xue; Mikhail Yurochkin; Yuekai Sun

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arXiv · 2003.05048

Auditing ML Models for Individual Bias and Unfairness

Songkai Xue·Mikhail Yurochkin·Yuekai Sun

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Abstract

We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal value. Our tools permit us to obtain asymptotic confidence intervals and hypothesis tests that cover the target/control the Type I error rate exactly. To demonstrate the utility of our tools, we use them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.

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Songkai Xue, Mikhail Yurochkin, Yuekai Sun. 2020-03-11. Auditing ML Models for Individual Bias and Unfairness. https://arxiv.org/abs/2003.05048

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