Can Legislation Be Made Machine-Readable in PROLEG?

TL;DR

This paper proposes a framework using large language models and formal legal representations to convert regulatory texts into machine-readable, executable PROLEG programs, enhancing legal reasoning and decision explanation.

Contribution

It introduces a novel end-to-end process for transforming legal texts into formal, executable representations using LLMs and PROLEG, with validation by legal experts.

Findings

Successful transformation of GDPR Article 6 into PROLEG code

Generation of human-readable explanations from executable PROLEG programs

Identification of limitations and future directions for automating legal formalization

Abstract

The anticipated positive social impact of regulatory processes requires both the accuracy and efficiency of their application. Modern artificial intelligence technologies, including natural language processing and machine-assisted reasoning, hold great promise for addressing this challenge. We present a framework to address the challenge of tools for regulatory application, based on current state-of-the-art (SOTA) methods for natural language processing (large language models or LLMs) and formalization of legal reasoning (the legal representation system PROLEG). As an example, we focus on Article 6 of the European General Data Protection Regulation (GDPR). In our framework, a single LLM prompt simultaneously transforms legal text into if-then rules and a corresponding PROLEG encoding, which are then validated and refined by legal domain experts. The final output is an executable PROLEG program that can produce human-readable explanations for instances of GDPR decisions. We describe processes to support the end-to-end transformation of a segment of a regulatory document (Article 6 from GDPR), including the prompting frame to guide an LLM to "compile" natural language text to if-then rules, then to further "compile" the vetted if-then rules to PROLEG. Finally, we produce an instance that shows the PROLEG execution. We conclude by summarizing the value of this approach and note observed limitations with suggestions to further develop such technologies for capturing and deploying regulatory frameworks.