On the Legal Compatibility of Fairness Definitions
It addresses a critical gap for researchers and practitioners in AI fairness by highlighting legal incompatibilities, though it is incremental in building on prior critiques.
The paper tackles the misalignment between machine learning fairness definitions and U.S. anti-discrimination law, demonstrating that these definitions often misunderstand and inappropriately co-opt legal concepts, and discusses lessons for both communities.
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.