Legal interpretation and AI: from expert systems to argumentation and LLMs
This paper provides a historical and methodological overview of AI's engagement with legal interpretation, which is significant for researchers and practitioners in AI and Law seeking to understand the field's development and current trends.
This paper reviews the evolution of AI and Law research concerning legal interpretation, tracing its progression from expert systems focused on knowledge transfer to argumentation-based approaches for assessing interpretive claims, and finally to machine learning models, including LLMs, for generating interpretive suggestions.
AI and Law research has encountered legal interpretation in different ways, in the context of its evolving approaches and methodologies. Research on expert system has focused on legal knowledge engineering, with the goal of ensuring that human-generated interpretations can be precisely transferred into knowledge-bases, to be consistently applied. Research on argumentation has aimed at representing the structure of interpretive arguments, as well as their dialectical interactions, to assess of the acceptability of interpretive claims within argumentation frameworks. Research on machine learning has focused on the automated generation of interpretive suggestions and arguments, through general and specialised language models, now being increasingly deployed in legal practice.