Patrick Barry (U Michigan Law) has posted “Digital Lawyering: Advocacy in the Age of AI” (Michigan Technology Law Review Forthcoming) on SSRN. Here is the abstract:

All lawyers are now digital lawyers. From Zoom hearings, to e-discovery, to AI-enhanced research and writing, the practice of law increasingly requires the skillful navigation of a wide range of technological tools. It’s no longer enough to be book smart and street smart. More and more, you also have to be byte-smart.

To help future lawyers navigate this transition, I recently created a course at both the University of Michigan Law School and the University of Chicago Law School called “Digital Lawyering: Advocacy in the Age of AI.” The course takes a skill-building approach to artificial intelligence. Which tools are worth using? What questions are worth asking? And how do advocates of all kinds continue to add value to clients—and promote justice—in a world increasingly populated by chatbots, algorithms, and a wide range of other powerful digital products?

This paper collects thoughts from the presentation about the course that I delivered at the “Law and Justice in the Age of AI” symposium organized by the Michigan Technology Law Review on November 18, 2023

Jaromir Savelka (Carnegie Mellon University) and Kevin Ashley (U Pitt Law) have posted “The Unreasonable Effectiveness of Large Language Models in Zero-Shot Semantic Annotation of Legal Texts” (Frontiers in Artificial Intelligence, Vol. 6, p. 1, 2023) on SSRN. Here is the abstract:

The emergence of ChatGPT has sensitized the general public, including the legal profession, to large language models’ (LLMs) potential uses (e.g., document drafting, question answering, and summarization). Although recent studies have shown how well the technology performs in diverse semantic annotation tasks focused on legal texts, an influx of newer, more capable (GPT-4) or cost-effective (GPT-3.5-turbo) models requires another analysis. This paper addresses recent developments in the ability of LLMs to semantically annotate legal texts in zero-shot learning settings. Given the transition to mature generative AI systems, we examine the performance of GPT-4 and GPT-3.5-turbo(-16k), comparing it to the previous generation of GPT models, on three legal text annotation tasks involving diverse documents such as adjudicatory opinions, contractual clauses, or statutory provisions. We also compare the models’ performance and cost to better understand the trade-offs. We found that the GPT-4 model clearly outperforms the GPT-3.5 models on two of the three tasks. The cost-effective GPT-3.5-turbo matches the performance of the 20× more expensive text-davinci-003 model. While one can annotate multiple data points within a single prompt, the performance degrades as the size of the batch increases. This work provides valuable information relevant for many practical applications (e.g., in contract review) and research projects (e.g., in empirical legal studies). Legal scholars and practicing lawyers alike can leverage these findings to guide their decisions in integrating LLMs in a wide range of workflows involving semantic annotation of legal texts.

Harry Surden (U Colorado Law) has posted “Computable Law and Artificial Intelligence” (Cambridge Handbook of Private Law and Artificial Intelligence (forthcoming 2024)) on SSRN. Here is the abstract:

This article explores the theory and application of “Computable Law”.

‘Computable Law’ is a research area focused on the creation and use of computer models of laws.

What does it mean to model a law computationally? There are a few broad approaches. In one method, researchers begin with traditional, written legal sources of law – such as statutes, contracts, administrative regulations, and court opinions – and identify legal rules that they wish to model. They then aim to ‘translate’ aspects of these legal obligations into comparable sets of organised data, programming instructions, and other forms of expression that computers can easily process. In that approach, one begins with a familiar legal text written in a ‘natural language’ such as English, and then aims to represent qualities of the legal obligations described – such as their structure, meaning, or application – in terms of data, programming rules and other highly organised forms of expression that are easier for computers to handle.

The other approach allows us to express legal obligations as data from the outset. There, one begins with laws expressed as computer data in their initial form – a departure from the written-language through which laws have traditionally been conveyed. An example of this approach can be found in the so-called data-oriented,‘computable contracts’.

These are legal agreements created electronically, whose core terms are expressed largely as data rather than as written paragraphs, and which are frequently used in finance, electronic commerce, cryptocurrency, and other areas.

Through this ‘data-oriented’ method we are still ultimately able to display legal obligations in forms that people can understand, such as in language or visually on a computer screen. However, what is interesting is that the human-understandable versions are typically derived upwards from the underlying data. In other words, one can present to users what appear to be ordinary written legal documents on a screen or on paper, but the contents of those documents are actually generated by processing lower-level computer data. In those cases, it is sometimes best to think of the law’s native data-oriented representation as the authoritative version (or source of ‘ground-truth’) for information about the legal obligations.

John Bliss (U Denver Law) has posted “Teaching Law in the Age of Generative AI” (Jurimetrics, forthcoming) on SSRN. Here is the abstract:

With the rise of large language models capable of passing law school exams and the Unified Bar Exam, how should legal educators prepare their students for an age of transformative AI advances? Text-generating AI is poised to become a standard tool of legal research and writing, as it is being integrated in legal research and word processing applications (such as LexisNexis and Microsoft Word) that automate the drafting of legal documents based on human prompts. This Article explores the implications of these developments for legal education, focusing on pedagogy, curriculum, and assessment.

The Article draws from four key perspectives relevant to the use of generative AI in law teaching: a survey of law students who participated in an AI-integrated course; a national survey of law faculty; an overview of the current state and projected futures of AI in the legal profession; and a summary of findings from the remarkably extensive educational literature that has arisen around the globe exploring the use of ChatGPT in different teaching contexts. These perspectives tend to support the development of an AI-integrated legal education. Yet, most of the surveyed law faculty, even those who strongly agreed that students should be prepared to use and critically evaluate generative AI, emphasized that they were uninformed about this technology and unsure how to proceed.

This Article provides guidance, recommending that legal educators begin teaching with emerging AI tools, while exploring how implementation might vary across the legal curriculum. These recommendations are based on a number of factors, including consideration of how AI-integrated teaching may affect emerging professional competencies, traditional learning goals, academic integrity, and equity among students. The Article concludes by offering practical suggestions for incorporating generative AI in law teaching, including examples of exercises where students collaborate with generative AI in their writing, evaluate AI outputs, create their own AI tutors and debate partners, role-play with chatbots in classroom simulations, and reflect on the responsible use of generative AI in the legal profession.

Michael L. Smith (St. Mary’s U School of Law) has posted “Generative AI in the Attorney-Client Relationship: An Exercise in Critical Revision and Client Management” on SSRN. Here is the abstract:

Discussions of generative AI in legal practice and education often assert that this technology will lead to a sea change in legal writing, research, and revision. While some of the more breathless proclamations deserve skepticism, there’s little doubt that this technology may generate new forms of headaches for those in the legal field – particularly in the hands of clients or opposing counsel who attempt to use this technology to save the time, money, and effort required for complex legal tasks.

To that end, this essay proposes an exercise template for law students which illustrates how generative AI technology may be misused or abused. Presenting students with an AI-generated motion and asking them to reason through a scenario in which a hypothetical client demands that they file the motion tests a number of skills. First, students must critically read and revise the motion – noting shortcomings in AI-generated legal writing and identifying the confident mistakes that permeate the output. Second, and perhaps even more importantly, students must think through how to communicate these mistakes to a stubborn client, requiring them to consider client relationships and motivations and to communicate complex information in a simple, concise, and diplomatic manner. Doing so takes the exercise beyond a practical test of doctrine and legal writing, and engages students with deeper questions of empathizing with client needs, developing their professional identity, and preparing for a world in which generative AI will not only be used, but also abused.

Keith Swisher (U Arizona Law) has posted “The Right to (Human) Counsel: Real Responsibility for Artificial Intelligence” (74 S.C. L. Rev. 823 (2023)) on SSRN. Here is the abstract:

The bench and bar have created and enforced a comprehensive system of ethical rules and regulation. In many respects, it is a unique and laudable system for regulating and guiding lawyers, and it has taken incremental measures to account for the wave of new technology involved in the practice of law. But it is not ready for the future. It rests on an assumption that humans will practice law. Although humans might tinker at the margins, review work product, or serve some other useful purposes, they likely will not be the ones doing most of the legal work in the future. Instead, AI counsel will be serving the public. For the system of ethical regulation to serve its core functions in the future, it needs to incorporate and regulate AI counsel. This will necessitate, among other things, bringing on new disciplines in the drafting of ethical guidelines and in the disciplinary process, along with a careful review and update of the ethical rules as applied to AI practicing law.

Colleen Medill (University of Nebraska at Lincoln – College of Law) has posted “Integrating Artificial Intelligence Tools into the Formation of Professional Identity” on SSRN. Here is the abstract:

My claim in this Article is that a lawyer’s personal use of artificial intelligence (AI) in the practice of law is now an essential component of a lawyer’s professional identity that must be intentionally developed as a law student before entering the practice of law. After demonstrating the strong connection between the use of AI tools in legal practice, the requirement of lawyer competence, and the formation of professional identity, the Article proposes four “best practices” principles for integrating AI tools with traditional lawyering skills exercises to assist students in the formation of professional identity. The Article concludes with an example that can be used in the first-year Property course.

Piotr Bystranowski (Interdisciplinary Centre for Ethics; Jagiellonian University) and Kevin Tobia
Georgetown University Law Center; Georgetown University – Department of Philosophy) have posted “Measuring Meta-Interpretation” (Journal of Institutional and Theoretical Economics (Forthcoming)) on SSRN. Here is the abstract:

American legal interpretation has taken an empirical turn. Courts and scholars use corpus linguistics, survey experiments, and machine learning to clarify legal texts’ meanings. We introduce these developments in “issue-level interpretation,” concerning interpretive theories’ application to legal language. Empirical methods also inform “meta-interpretative” debate: Which interpretive theory do interpreters use; which have they used; and which should they use? We demonstrate machine learning’s relevance to these meta-interpretive debates with insights provided by word embeddings that we trained on a corpus of over 1.3 million U.S. federal court decisions.

Maura R. Grossman (U Waterloo; York U Osgoode Hall), Paul W. Grimm (Duke Law), Daniel G. Brown (U Waterloo), and Molly Xu (U Waterloo) have posted “The GPTJudge: Justice in a Generative AI World” (Duke Law & Technology Review 2023) on SSRN. Here is the abstract:

Generative AI (“GenAI”) systems such as ChatGPT recently have developed to the point where they are capable of producing computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to hire forensic experts to address AI- generated evidence, the ability of juries to discern authentic from fake evidence, and whether GenAI will overwhelm the courts with AI-generated lawsuits, whether vexatious or otherwise. GenAI systems have the potential to challenge existing substantive intellectual property (“IP”) law by producing content that is machine, not human, generated, but that also relies on human-generated content in potentially infringing ways. Finally, GenAI threatens to alter the way in which lawyers litigate and judges decide cases.

This article discusses these issues, and offers a comprehensive, yet understandable, explanation of what GenAI is and how it functions. It explores evidentiary issues that must be addressed by the bench and bar to determine whether actual or asserted (i.e., deepfake) GenAI output should be admitted as evidence in civil and criminal trials. Importantly, it offers practical, step-by- step recommendations for courts and attorneys to follow in meeting the evidentiary challenges posed by GenAI. Finally, it highlights additional impacts that GenAI evidence may have on the development of substantive IP law, and its potential impact on what the future may hold for litigating cases in a GenAI world.

Natalie Pierce (Gunderson Dettmer; Columbia Law School; UC Berkeley) and Stephanie Goutos (Gunderson Dettmer; Albany Law School; The College of Saint Rose) have published “Why Law Firms Must Responsibly Embrace Generative AI” on SSRN. Here is the abstract:

In the era of artificial intelligence (AI), the professional axiom stands truer than ever: “AI won’t replace lawyers, but lawyers who use AI will replace lawyers who don’t.” This paper is a must-read for any forward-thinking law firm seeking to outpace competition and excel in an AI-augmented world, all while upholding the professional standards of ethical and client service that is not only competent, but exceptional.

The transformative impact of generative artificial intelligence (GAI) on the legal industry is inevitable, a change predicted to fuel global GDP growth by almost $7 trillion over the next decade. Amid growing concerns and even some calls for an outright prohibition of GAI in law firms, we argue for a balanced, responsible embrace of this technology. This stance, we believe, is imperative for the future of the legal profession and can position legal professionals at the forefront of innovation and client service. We provide several real-world examples of how organizations, including law firms, are already successfully leveraging GAI.

Our paper highlights how GAI, augmented with human input, can greatly improve the legal sector. Federal courts’ recent acknowledgement of GAI’s role in litigation, with requirements for its explicit disclosure, points towards its future ubiquity. In navigating this shift, we emphasize that lawyers must uphold their ethical standards and obligations outlined in the Model Rules of Professional Conduct.

We address and confront counterarguments suggesting GAI’s unsuitability for legal work, potential for ethics violations, and risks of inaccuracies, bias, privacy breaches, and legal risks. Acknowledging the inherent risks, we present strategies to mitigate these and continue competently delivering exceptional client service.

We also underline the risks of not using GAI, from potential unauthorized data disclosure to possible reputational damage and competitive disadvantage in the legal industry. Highlighting the importance of responsible GAI usage, we present a comprehensive list of the Top 10 best practices for its implementation within law firms. We conclude by stressing that legal professionals’ refusal to adopt GAI could lead to their obsolescence, predicting the prevalence of GAI policies across U.S. industries, including the legal sector, by 2023 end.