Publication
AISTATS 2020
Conference paper

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.

Date

Publication

AISTATS 2020

Authors

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