# Report of the 1st Workshop on Generative AI and Law

arXiv: https://arxiv.org/abs/2311.06477 · HTML: https://mlsanity.com/paper/2311.06477/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2023.

## Built on by (most cited first, 13 of 13)

- [Rethinking LLM Memorization through the Lens of Adversarial Compression](https://mlsanity.com/paper/2404.15146/) (2024)
- [Measuring memorization in language models via probabilistic extraction](https://mlsanity.com/paper/2410.19482/) (2024)
- [Extracting books from production language models](https://mlsanity.com/paper/2601.02671/) (2026)
- [Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research](https://mlsanity.com/paper/2412.06966/) (2024)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation](https://mlsanity.com/paper/2507.05578/) (2025)
- [On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI](https://mlsanity.com/paper/2402.05979/) (2024)
- [Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios](https://mlsanity.com/paper/2407.19760/) (2024)
- [Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://mlsanity.com/paper/2510.18333/) (2025)
- [Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Models](https://mlsanity.com/paper/2511.20799/) (2025)
- [Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models](https://mlsanity.com/paper/2506.19881/) (2025)
- [Estimating near-verbatim extraction risk in language models with decoding-constrained beam search](https://mlsanity.com/paper/2603.24917/) (2026)
- [Extractable Memorization From First Principles](https://mlsanity.com/paper/2607.12649/) (2026)

## Abstract (arXiv)

This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw), held in July 2023. A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI, and by Generative AI for law, with an emphasis on U.S. law in particular. We begin the report with a high-level statement about why Generative AI is both immensely significant and immensely challenging for law. To meet these challenges, we conclude that there is an essential need for 1) a shared knowledge base that provides a common conceptual language for experts across disciplines; 2) clarification of the distinctive technical capabilities of generative-AI systems, as compared and contrasted to other computer and AI systems; 3) a logical taxonomy of the legal issues these systems raise; and, 4) a concrete research agenda to promote collaboration and knowledge-sharing on emerging issues at the intersection of Generative AI and law. In this report, we synthesize the key takeaways from the GenLaw workshop that begin to address these needs. All of the listed authors contributed to the workshop upon which this report is based, but they and their organizations do not necessarily endorse all of the specific claims in this report.