Three Laws Blog: Law and Artificial Intelligence

Duryea on What Generative AI Means for Clinical Legal Education @pelfreyduryea @BU_Law

Danielle Pelfrey Duryea, Boston University School of Law, has published What Does Generative Artificial Intelligence Mean for Clinical Legal Education?

Within the last ten months, OpenAI's ChatGPT and other large language model (LLM) artificial intelligence tools have become widely available, widely celebrated, and widely feared. The legal profession is abuzz with think-pieces on how AI will (or won't) transform the practice of law. Legal academia has begun to digest how these extremely rapidly-evolving tools could change legal education and licensure, including studying practices (e.g., Harrington), law school exams (e.g., Choi, Hickman, Monahan & Schwarcz), legal research and writing (e.g., Romig; Bishop; Baskaran; Smith), podium teaching and academic service (e.g., Pettinato Oltz), and the bar exam (e.g., Williams; Bommarito & Katz). This paper continues this urgent reflection by considering how AI tools change the context for and practice of conventional goals and methods of clinical legal education, including client-centered / collaborative lawyering, from-scratch drafting of legal documents, professional collaboration, etc.

The full text is not available from SSRN. 

A New Blog On AI and Legal Education @theFordon @SeanLovesBooks @JenWondracek @AZSarahMom

Check out AI Law Librarians, this new blog on generative AI and legal education, research, and tech, from some very smart and very entertaining law librarians. 

Lucchi on ChatGPT: A Case Study on Copyright Challenges for Generative AI Systems

Nicola Lucchi, Universitat Pompeu Fabra, Department of Law, has published ChatGPT: A Case Study on Copyright Challenges for Generative AI Systems at 2023 European Journal of Risk Regulation 1. Here is the abstract.

This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems, with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate high-quality outcomes, generative artificial intelligence (AI) systems necessitate substantial quantities of training data, which may frequently comprise copyright protected information. This prompts inquiries into the legal principles of fair use, the creation of derivative works, and the lawfulness of data gathering and utilization. The utilization of input data for the purpose of training and enhancing AI models presents significant concerns regarding potential violations of copyright. This paper offers suggestions for safeguarding the interests of copyright holders and competitors, while simultaneously addressing legal challenges and expediting the advancement of artificial intelligence technologies. This study analyzes the ChatGPT platform as a case example to explore the necessary modifications that copyright regulations must undergo to adequately tackle the intricacies of authorship and ownership in the realm of AI-generated creative content. Note: Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Download the article from SSRN at the link.

Choi and Schwarcz on AI Assistance in Legal Analysis: An Empirical Study @UofMNLawSchool @Dschwarcz

Jonathan H. Choi and Daniel Schwarcz, both of the University of Minnesota Law School, have published AI Assistance in Legal Analysis: An Empirical Study as Minnesota Legal Studies Research Paper No. 23-22. Here is the abstract.

Can artificial intelligence (AI) augment human legal reasoning? To find out, we designed a novel experiment administering law school exams to students with and without access to GPT-4, the best-performing AI model currently available. We found that assistance from GPT-4 significantly enhanced performance on simple multiple-choice questions but not on complex essay questions. We also found that GPT-4’s impact depended heavily on the student’s starting skill level; students at the bottom of the class saw huge performance gains with AI assistance, while students at the top of the class saw performance declines. This suggests that AI may have an equalizing effect on the legal profession, mitigating inequalities between elite and nonelite lawyers. In addition, we graded exams written by GPT-4 alone to compare it with humans alone and AI-assisted humans. We found that GPT-4’s performance varied substantially depending on prompting methodology. With basic prompts, GPT-4 was a mediocre student, but with optimal prompting it outperformed both the average student and the average student with access to AI. This finding has important implications for the future of work, hinting that it may become advantageous to entirely remove humans from the loop for certain tasks.

Download the article from SSRN at the link.

Volokh on Large Libel Models? Liability for AI Output @unsealiecourt @JournalSpeech

Eugene Volokh, University of California, Los Angeles, School of Law, has published Large Libel Models? Liability for AI Output at 3 Journal of Free Speech Law 489 (2023). Here is the abstract.

“Large Language Model” AI programs routinely invent false and defamatory allegations, complete with invented quotes and invented newspaper articles. Indeed, lawsuits have already been filed over alleged libels created by ChatGPT and Bing. Should such AI programs’ creators be liable for defamation, based on their programs’ output? In this article, I begin by analyzing this question under the current rules of U.S. defamation law. I will tentatively argue that, when the “actual malice” standard applies, the standard might be satisfied if an AI company has received actual notice of particular spurious information being produced by its software but has refused to act. This would in practice require such companies to implement a “notice-and-blocking” system, loosely similar to “notice-and-takedown” systems required under the DMCA as to copyright and trademark infringements. And I will also discuss the possibility of negligence liability, when such liability is authorized under libel law, by analogy to negligent design product liability. To be sure, allowing such liability could yield substantial costs. That is particularly so since it may require lay judges and juries to evaluate complicated technical claims about which designs are feasible. (Such concerns of course mirror the concerns about legal liability as to other products, such as pharmaceuticals or cars, or as to services, such as surgical procedures.) Part II will tentatively discuss some arguments for why the law might be changed, whether by courts, by legislatures, or by administrative agencies. Finally, Part III will offer some similarly tentative thoughts about how this might apply to other claims, such as false light, disclosure of private facts, the right of publicity, or negligence.

Download the article from SSRN at the link.