Challenges for generative AI in legal reasoning

Abstract

Large Language Models (LLMs) are being integrated into professional domains, yet their limitations in such high-stakes fields as law remain poorly understood. In response, this paper introduces examples of critical challenges to the functioning of generative and other forms of artificial intelligence (AI) as reliable reasoning tools in judicial decision-making. The study deconstructs core requirements and challenges for AI, including the ability to select the correct legal framework across jurisdictions, generate sound arguments based on the doctrine of the sources of law, distinguish ratio decidendi and obiter dicta in case law, resolve ambiguity arising from general clauses like “reasonableness”, manage conflicting legal provisions, and apply the burden of proof correctly. The paper then maps various AI enhancement mechanisms, such as retrieval-augmented generation (RAG), multi-agent systems and neuro-symbolic AI, to these challenges, assessing their potential to bridge the gap between the probabilistic nature of LLMs and the rigorous, choice-driven demands of legal interpretation. Furthermore, the paper sketches a path towards an evaluation framework, proposing that legal requirements be organized into normative, doctrinal, evidential, and technical categories, and subsequently operationalized into domain-specific, testable design obligations. The findings indicate that these techniques can address specific narrow challenges, but they fail to solve the more significant ones that remain, particularly in tasks requiring discretion and transparent, justifiable reasoning. Therefore, we advocate for a staged adoption, first capturing efficiency in simple cases with technology already available today and thereafter sustaining long-term investment in new methods that handle hierarchy, temporality, and other requirements of legally sound reasoning, thus enabling expansion to complex adjudication in the future.