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.

Iaia on To Be, or Not To Be...Original Under Copyright Law, That Is (One of) the Main Questions Concerning AI-Produced Works @UniLUISS @GrurInt

Vincenzo Iaia, Luiss University, has published To Be, or Not to Be … Original Under Copyright Law, That Is (One of) the Main Questions Concerning AI-Produced Works at 71 GRUR International IX (2022). Here is the abstract.

This paper examines whether different types of AI-produced works, namely AI-assisted, AI-generated, and AI-genassisted works, are able to satisfy the originality requirement, as established within the EU copyright acquis. It will argue that even assuming that the choices of AIs can be deemed free, they could not be considered creative due to the anthropological footprint of copyright law which traditionally covers only human creativity. This implies that copyright protection can be granted only to outputs that display a sufficient level of original human intervention, which is most likely present in AI-assisted works and, depending on the case, in AI-genassisted works, too. The desirability of stretching copyright further to accommodate artificial creativity is also lessened by the risk that it could saturate the creative market due to an overexpansion of monopoly rights. Thus, the copyright solution will be evaluated together with some existing alternative legal tools, such as neighbouring rights, sui generis rights, patents, or even the absence of protection over AI-generated works to preserve a robust public domain.

The full text is not available for download from SSRN.

Asil and Wollmann on Whether Machines Can Commit Crimes Under US Antitrust Laws

Aslihan Asil, Yale University, and Thomas Wollmann, University of Chicago, have published Can Machines Commit Crimes Under US Antitrust Laws? Here is the abstract.

Generative artificial intelligence is being rapidly deployed for corporate tasks including pricing. Suppose one of these machines communicates with the pricing manager of a competing firm, proposes to collude, receives assent, and raises price. Is this a crime under US antitrust laws, and, if so, who is liable? Based on the observed behavior of the most widely adopted large language model, we argue that this conduct is imminent, would satisfy the requirements for agreement and intent under Section 1 of the Sherman Act, and confer criminal liability to both firms as well as the pricing manager of the competing firm.

Download the article from SSRN at the link.

Joh on Equitable Legal Remedies and the Existential Threat to Generative AI @elizabeth_joh @UCDavisLaw

Elizabeth E. Joh, University of California, Davis, School of Law, Equitable Legal Remedies and the Existential Threat to Generative AI. Here is the abstract.

Generative AI programs like ChatGPT and Midjourney can produce text, images, and video because their models have been trained on vast amounts of books, pictures, art, and other media found on the internet. Training a large language model like ChatGPT, for instance, to “learn” patterns and relationships in billions of words allows these models to produce written text responsive to prompts. But these models have trained on digital information like novels and original art without obtaining permission. Unsurprisingly, the release of these generative AI tools has already prompted lawsuits by individuals and companies alleging that the use of their artistic works in the development of these models violates copyright law. Whether reliance on this type of training data violates copyright, and whether the outputs based on the training data constitute infringing derivative works are open and difficult questions. But these copyright claims also raise a separate issue: can plaintiffs seek remedies that would destroy generative AI programs?

Download the essay from SSRN at the link.

Cooney on Teaching AI to Use Plain Language @jmarkcooney @WMUcooleylaw

Mark Cooney, Western Michigan University Cooley Law School, has published Teaching AI to Use Plain Language at 102 Michigan Bar Journal 26 (July/August 2023). Here is the abstract.

This article demonstrates how lawyers can use generative AI to reimagine and improve old forms. It begins with a dense guaranty form and shows two AI redrafts, complete with the author's prompts and the final results.

Download the article from SSRN at the link.

Broyde on AI and Jewish Law: Seeing How ChatGPT 4.0 Looks at a Novel Issue @EmoryLaw

Michael J. Broyde, Emory University School of Law, is publishing AI and Jewish Law: Seeing How ChatGPT 4.0 Looks at a Novel Issue in B.D.D.- Bekhol Derakhekha Daehu. Here is the abstract.

In an article forthcoming, I analyze whether a man who is in a same sex relationship may engage in the priestly blessing as a matter of Jewish Law and the article concludes that such is permitted in Jewish Law. This is a novel question of Jewish Law, with virtually no prior works written that directly discuss this point. Furthermore, nothing that has been published can be found on the internet, as far as I can determine. I was interested in seeing how ChatGPT 4.0 thought about this issue after seeing the sources I saw. So, I uploaded all the text in this article one-by-one and asked ChatGPT 4.0 to translate them as an expert for a Jewish law audience. It did a very good – but not perfect – job. I then asked ChatGPT 4.0 “Now that you have seen all these texts, do you think a man who is a priest and in a same sex marriage can perform the priestly blessing as a matter of halacha. Write me about 500 words.” There is virtually no discussion of the issue of a same sex married kohen engaging in the priestly blessing in either Hebrew or English on the internet and none of the sources I uploaded discuss this issue. ChatGPT had to derive and reason this from the sources I uploaded (and which it knew from other sources) about what was the Jewish law in this case. It could not simply “google” to find out what others have said, as this is a matter of first impression. Indeed, what is needed to answer this question is nuanced reading of sources about the various categories present and the classification of sins into proper groupings, as well as other ideas, as my article shares. I was quite surprised how excellent a job ChatGPT did. Furthermore, when I asked it in the end to state a contrary view, it highlighted the correct issue and focused on it. This article – after sharing ChatGPT 4.0 various answers to questions I posed from the sources I shared – ends with a complex discussion of whether Jewish law and American law will be subject to AI analysis and in what way is Jewish Law different from American Law.

Download the article from SSRN at the link.