  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.
