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

2026-03-05

AI Summary

This study addresses the challenge of effectively modeling legal interpretation—a complex, dynamic, and highly context-dependent cognitive process—within artificial intelligence. By systematically integrating three major paradigms in legal AI, namely knowledge-engineered expert systems, structured computational argumentation models (such as Dung-style semantics), and large language model (LLM)-driven interpretive generation, the work presents the first unified framework for legal interpretation. The research elucidates the complementary strengths and respective boundaries of these paradigms and proposes a synergistic integration pathway. This advances legal AI beyond mere rule compliance toward interpretable and contestable reasoning mechanisms, thereby establishing both a theoretical foundation and a technical roadmap for developing intelligent systems aligned with the practical demands of legal reasoning.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• legal interpretation
• AI and Law
• expert systems
• argumentation
• language models

Innovation

• legal interpretation
• expert systems
• argumentation frameworks
• large language models
• AI and Law