Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System

2026-04-28

AI Summary

This study addresses the growing complexity of global AI regulations and the challenges in efficiently accessing and interpreting cross-jurisdictional regulatory information. To this end, the authors develop a retrieval-augmented generation (RAG) system tailored for multi-jurisdictional AI regulation, integrating 242 regulatory documents from 68 jurisdictions. The work introduces a type-aware structured chunking method, a conditional retrieval routing mechanism leveraging named entity recognition and metadata, and a re-ranking strategy prioritizing legislative status and source authority. Evaluated on 50 queries, the system achieves an average factual faithfulness of 0.87 and answer relevance of 0.84, with single-entity queries reaching 0.92 in relevance and multi-jurisdictional comparison queries attaining 0.88 in faithfulness, thereby demonstrating the efficacy of domain-specific retrieval strategies.

Abstract

Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we present a multi-jurisdictional Retrieval-Augmented Generation system for global AI regulation. Our corpus includes 242 documents across 68 jurisdictions, ranging from formal legislation like the EU AI Act to unstructured policy documents such as national AI strategies. The system makes three technical contributions: type-specific chunking that preserve legal structure across heterogenous documents; conditional retrieval routing with entity detection and metadata for legal citations; and priority-based re-ranking to boost enacted legislation over policy and secondary sources. Evaluation of 50 queries reveals strong performance across both single-entity and multi-jurisdictional questions, achieving 0.87 average faithfulness and 0.84 average answer relevancy. Single-entity queries achieve 0.86 average faithfulness and 0.92 average answer relevancy, while multi-jurisdictional comparison queries achieve 0.88 average faithfulness and 0.75 average answer relevancy. These findings highlight the effectiveness of domain-specific retrieval strategies for navigating complex, heterogenous regulatory corpora.

Problem

• AI regulation
• multi-jurisdictional
• legal compliance
• regulatory heterogeneity
• global policy

Innovation

• Retrieval-Augmented Generation
• multi-jurisdictional
• legal document processing
• conditional retrieval routing
• priority-based re-ranking