Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference

TL;DR

This paper introduces a Bayesian framework that leverages unlabeled data to improve the accuracy and confidence in fairness metric estimation, especially when labeled data is scarce.

Contribution

It presents a novel hierarchical Bayesian model that combines labeled and unlabeled data to produce more reliable fairness assessments with quantified uncertainty.

Findings

Significant reduction in estimation error across multiple datasets

Improved fairness metric estimates with lower variance

Effective use of unlabeled data enhances fairness evaluation

Abstract

We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful. We propose a general Bayesian framework that can augment labeled data with unlabeled data to produce more accurate and lower-variance estimates compared to methods based on labeled data alone. Our approach estimates calibrated scores for unlabeled examples in each group using a hierarchical latent variable model conditioned on labeled examples. This in turn allows for inference of posterior distributions with associated notions of uncertainty for a variety of group fairness metrics. We demonstrate that our approach leads to significant and consistent reductions in estimation error across multiple well-known fairness datasets, sensitive attributes, and predictive models. The results show the benefits of using both unlabeled data and Bayesian inference in terms of assessing whether a prediction model is fair or not.