On the Legal Compatibility of Fairness Definitions

TL;DR

This paper examines the misalignment between machine learning fairness definitions and U.S. anti-discrimination law, highlighting legal misunderstandings and suggesting lessons for both communities.

Contribution

It reveals how ML fairness definitions often misinterpret legal concepts, emphasizing the need for better alignment with legal standards in fairness research.

Findings

Examples of legal and ML terminology misalignment

Discussion on legal and ML fairness community lessons

Analysis focused on U.S. anti-discrimination law

Abstract

Past literature has been effective in demonstrating ideological gaps in machine learning (ML) fairness definitions when considering their use in complex socio-technical systems. However, we go further to demonstrate that these definitions often misunderstand the legal concepts from which they purport to be inspired, and consequently inappropriately co-opt legal language. In this paper, we demonstrate examples of this misalignment and discuss the differences in ML terminology and their legal counterparts, as well as what both the legal and ML fairness communities can learn from these tensions. We focus this paper on U.S. anti-discrimination law since the ML fairness research community regularly references terms from this body of law.