Legal interpretation and AI: from expert systems to argumentation and LLMs

TL;DR

This paper reviews the evolution of AI approaches in legal interpretation, from expert systems and argumentation frameworks to modern large language models, highlighting their methodologies and applications.

Contribution

It provides a comprehensive overview of how AI methods have developed in legal interpretation, comparing traditional and modern techniques.

Findings

Expert systems focus on knowledge encoding for consistent interpretation.

Argumentation models structure and evaluate interpretive debates.

Large language models automate interpretive suggestions in legal practice.

Abstract

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.