  Legal literature on machine learning (ML) tends to focus on harms, and thus
tends to reason about individual model outcomes and summary error rates. This
focus has masked important aspects of ML that are rooted in its reliance on
randomness -- namely, stochasticity and non-determinism. While some recent work
has begun to reason about the relationship between stochasticity and
arbitrariness in legal contexts, the role of non-determinism more broadly
remains unexamined. In this paper, we clarify the overlap and differences
between these two concepts, and show that the effects of non-determinism, and
consequently its implications for the law, become clearer from the perspective
of reasoning about ML outputs as distributions over possible outcomes. This
distributional viewpoint accounts for randomness by emphasizing the possible
outcomes of ML. Importantly, this type of reasoning is not exclusive with
current legal reasoning; it complements (and in fact can strengthen) analyses
concerning individual, concrete outcomes for specific automated decisions. By
illuminating the important role of non-determinism, we demonstrate that ML code
falls outside of the cyberlaw frame of treating ``code as law,'' as this frame
assumes that code is deterministic. We conclude with a brief discussion of what
work ML can do to constrain the potentially harm-inducing effects of
non-determinism, and we indicate where the law must do work to bridge the gap
between its current individual-outcome focus and the distributional approach
that we recommend.
