Auditing ML Models for Individual Bias and Unfairness

• URL: http://arxiv.org/abs/2003.05048v1
• Date: Wed, 11 Mar 2020 00:35:57 GMT
• Title: Auditing ML Models for Individual Bias and Unfairness
• Authors: Songkai Xue, Mikhail Yurochkin and Yuekai Sun
• Abstract summary: We formalize the task of auditing ML models for individual bias/unfairness and develop a suite of inferential tools for the optimal value. To demonstrate the utility of our tools, we use them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.
• Score: 46.94549066382216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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