On the Moral Justification of Statistical Parity

TL;DR

This paper explores the moral justification for enforcing statistical parity in algorithmic fairness, highlighting that the moral assumptions behind fairness metrics are often overlooked and examining when independence is morally appropriate.

Contribution

The paper extends previous work by analyzing the moral foundations of statistical parity and demonstrating through counterexamples that its justification is context-dependent.

Findings

Independence should be fulfilled only if differences are caused by unjust disparities or errors.

Counterexamples show that the moral justification for independence is not universally valid.

The moral basis for fairness metrics requires careful contextual consideration.

Abstract

A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.