On Fairness and Stability: Is Estimator Variance a Friend or a Foe?

• URL: http://arxiv.org/abs/2302.04525v1
• Date: Thu, 9 Feb 2023 09:35:36 GMT
• Title: On Fairness and Stability: Is Estimator Variance a Friend or a Foe?
• Authors: Falaah Arif Khan, Denys Herasymuk, Julia Stoyanovich
• Abstract summary: We propose a new family of performance measures based on group-wise parity in variance. We develop and release an open-source library that reconciles uncertainty quantification techniques with fairness analysis.
• Score: 6.751310968561177
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The error of an estimator can be decomposed into a (statistical) bias term, a variance term, and an irreducible noise term. When we do bias analysis, formally we are asking the question: "how good are the predictions?" The role of bias in the error decomposition is clear: if we trust the labels/targets, then we would want the estimator to have as low bias as possible, in order to minimize error. Fair machine learning is concerned with the question: "Are the predictions equally good for different demographic/social groups?" This has naturally led to a variety of fairness metrics that compare some measure of statistical bias on subsets corresponding to socially privileged and socially disadvantaged groups. In this paper we propose a new family of performance measures based on group-wise parity in variance. We demonstrate when group-wise statistical bias analysis gives an incomplete picture, and what group-wise variance analysis can tell us in settings that differ in the magnitude of statistical bias. We develop and release an open-source library that reconciles uncertainty quantification techniques with fairness analysis, and use it to conduct an extensive empirical analysis of our variance-based fairness measures on standard benchmarks.

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