Evolutionary Interpretation: Law and Machine Learning

Abstract

We approach the issue of interpretability in AI and law through the lens of evolutionary theory. From this perspective, blind or mindless ‘direct fitting’ is an iterative process through which a system and its environment are mutually constituted and aligned. The core case is natural selection. Legal reasoning can be understood as a step in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context. Machine learning, in so far as it relies on error correction through ‘backpropagation’, can be used to model the same process. It may therefore have value for understanding the long-run dynamics of legal and social change. This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in the context of the individual or instant case requires use of the higher-order cognitive capacities which human beings have evolved to understand their world and represent it through natural language. This type of forward propagation is unlikely, by its nature, to be well captured by machine learning approaches.