While statistical machine learning has advanced legal AI, its reliance on probabilities conflicts with some of the legal systems needs. This position paper argues that the strength of current AI tools, chiefly based on statistical learning, is also their main weakness in the legal domain. Instead, narrative-based approaches, inspired by Conviction Narrative Theory (CNT) and Algorithmic Information Theory (AIT), offer a better alternative. CNT explains how humans construct explanations and make decisions through coherent narratives, while AIT can be used to quantify the plausibility of these narratives. The paper discusses the applications of both paradigms to the legal domain and to legal AI, and in particular, how legal doctrine fits within CNT. Preliminary experiments on LLMs conclude this paper.
Holzenberger et al. (Wed,) studied this question.