GDPR Auto-Formalization with AI Agents and Human Verification

arXiv cs.AI / 4/17/2026

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Key Points

The paper investigates how to automatically formalize GDPR requirements using large language models while keeping humans in the loop to verify outputs.
It proposes a role-specialized multi-agent workflow where LLM components iteratively generate legal scenarios, formal rules, and atomic facts rather than attempting fully autonomous legal transformation.
The system couples AI generation with independent verification modules that check representational, logical, and legal correctness, including human reviewer assessments.
The authors build a high-quality dataset for GDPR auto-formalization and analyze both successful and problematic cases to understand failure modes.
Findings emphasize that structured verification and targeted human oversight are critical for reliable legal formalization, particularly when legal nuance and context-sensitive reasoning are involved.

Abstract

We study the overall process of automatic formalization of GDPR provisions using large language models, within a human-in-the-loop verification framework. Rather than aiming for full autonomy, we adopt a role-specialized workflow in which LLM-based AI components, operating in a multi-agent setting with iterative feedback, generate legal scenarios, formal rules, and atomic facts. This is coupled with independent verification modules which include human reviewers' assessment of representational, logical, and legal correctness. Using this approach, we construct a high-quality dataset to be used for GDPR auto-formalization, and analyze both successful and problematic cases. Our results show that structured verification and targeted human oversight are essential for reliable legal formalization, especially in the presence of legal nuance and context-sensitive reasoning.