Auditing ML models for individual bias and unfairness

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 inference 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 our them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.