Evaluating Fairness Using Permutation Tests

TL;DR

This paper introduces a permutation testing framework to statistically evaluate the fairness of machine learning models across different groups, providing a flexible tool for bias detection in various fairness metrics.

Contribution

It presents a novel permutation testing methodology for assessing model fairness across any chosen metric, enhancing bias detection capabilities for practitioners.

Findings

Effective bias detection across multiple fairness metrics

Flexible and formal hypothesis testing framework

Validated through extensive experiments

Abstract

Machine learning models are central to people's lives and impact society in ways as fundamental as determining how people access information. The gravity of these models imparts a responsibility to model developers to ensure that they are treating users in a fair and equitable manner. Before deploying a model into production, it is crucial to examine the extent to which its predictions demonstrate biases. This paper deals with the detection of bias exhibited by a machine learning model through statistical hypothesis testing. We propose a permutation testing methodology that performs a hypothesis test that a model is fair across two groups with respect to any given metric. There are increasingly many notions of fairness that can speak to different aspects of model fairness. Our aim is to provide a flexible framework that empowers practitioners to identify significant biases in any metric they wish to study. We provide a formal testing mechanism as well as extensive experiments to show how this method works in practice.