Anna Conley (U Montana Alexander Blewett III Law) has posted “Understanding the Duty of Competence for Attorneys Using Generative AI” on SSRN. Here is the abstract:

Ethical duties appear poised to be the primary regulatory tool for responsible use of generative AI (“GAI”) by attorneys. This reality necessitates a clear understanding of what the duty of competence requires for attorneys using GAI. Recent state bar and American Bar Association (“ABA”) guidance have coalesced around a foundational concept of informed decision-making, which requires that attorneys have sufficient knowledge about the GAI tool they are using and the specific task at hand to make an informed decision that employing the tool for that task is in the client’s best interests. Competence also requires attorneys avoid automation bias and mitigate against GAI tools’ limitations, including not only hallucinations but also incomplete, inaccurate and misgrounded outputs. Recent ethical guidance requires that attorneys retain cognitive agency when completing tasks that require human judgment and reasoning. These tasks render GAI’s “black box” or lack of explainability, particularly problematic and require attorneys to resist automation complacency, which refers to humans’ reduced capacity to understand or complete the tasks for which they rely on GAI.

Catherine Gage O’grady (U Arizona James E. Rogers College Law) and Casey O’grady (Harvard U Harvard Law) have posted “Agentic Workflows in the Practice of Law—AI Agents as Ethics Counsel” (Agentic Workflows in the Practice of Law—AI Agents as Ethics Counsel, 39 Geo. J. Legal Ethics (forthcoming 2025)) on SSRN. Here is the abstract:

Generative AI is reshaping legal practice as law firms invest in AI technology and prepare for a future where AI agents operate alongside human lawyers. While such a future raises numerous ethical concerns, it also opens new opportunities for ethical practice. This article explores the potential for AI agents to improve ethical decision-making within legal practices. We start by defining key concepts such as AI agents and agentic workflows. We then provide a brief overview of the role of ethics counsel in law firms and discuss critical behavioral challenges in the human practice of law.  Finally, we introduce a new model of AI agents acting as dedicated ethics counsel. These agents should be specialized, accountable, and systematic to provide comprehensive ethical guidance in a firm’s legal workflows. These ethical agents have the potential to mitigate the human biases in traditional legal practice and offer an efficient and scalable approach to ensure ethical compliance in an increasingly AI-driven legal landscape. To demonstrate this model, we end with real-world examples of what initial ethical agent contributions could look like in practice.

Eric A. Posner (U Chicago Law) and Shivam Saran (U Chicago Law) have posted “Judge AI: Assessing Large Language Models in Judicial Decision-Making” on SSRN. Here is the abstract:

Abstract. Can large language models (LLMs) replace human judges? By replicating a prior 2 x 2 factorial experiment conducted on 31 U.S. federal judges, we evaluate the legal reasoning of OpenAI’s GPT-4o. The experiment involves a simulated appeal in an international war crimes case, with two altered variables: the degree to which the defendant is sympathetically portrayed and the consistency of the lower court’s decision with precedent. We find that GPT-4o is strongly affected by precedent but not by sympathy, similar to students who were subjects in the same experiment but the opposite of the professional judges, who were influenced by sympathy. We try prompt engineering techniques to spur the LLM to act more like human judges, but with no success. “Judge AI” is a formalist judge, not a human judge.

Ben Davies (U Calgary Law) has posted “Ethics in Artificial Intelligence and Alternative Dispute Resolution: Generating AI/Human Reviewed Ethical Guidelines for ADR Practitioners and the Legal Profession” on SSRN. Here is the abstract:

This paper reviews current artificial intelligence guidelines, laws, rules, and standing orders imposed or provided by judges, attorneys, bar associations, and alternative dispute resolution institutions from both the United States and Canada.  Additionally, several key cases reviewing the use of generative AI in court filings are included and demonstrate the ethical and legal issues attorneys have already committed before a court.  After reviewing these rules and guidelines for mentions of alternative dispute resolution AI guidance, it is expanded upon by reviewing and generating, with the help of a fine-tuned Llama 3.1 model, a set of useful and detailed AI guidelines for alternative dispute resolution (ADR) practice.  To better explain AI and litigation analytics, there are sections covering the history of ADR and artificial intelligence from an ethical perspective, a brief section on AI based assistants for ADR practitioners, and a small section on the future of AI in dispute resolution.

Michael D. Murray (U Kentucky) has posted “Artificial Intelligence for Learning the Law: Generative AI for Academic Support in Law Schools and Universities – Report of Experiments” on SSRN. Here is the abstract:

This document reports research conducted from December 2022 to August 2024, and in particular, Part I experiments conducted from May 20 to July 12, 2024, and Part II from August 15-27, 2024, on the use of generative AI in legal education and academic support. This study was a cross-sectional, latitudinal, qualitative evaluation of generative AI systems at a certain point in time and at the level of development of each system at that point in time. Although the topic of this study is learning the law, the results and overall approach to using an AI as a personalized learning tutor can be applied to many graduate and undergraduate programs in universities and other levels of education. This paper reports the Part I experiments and their qualitative and comparative findings comparing the performance of public-facing general purpose LLMs—Claude 3.5 Sonnet, Copilot, Gemini 1.5 Pro, and GPT-4o Omni—and a law-specific LLM with a curated legal dataset, Lexis+ AI, and it will reveal which systems performed the best as personalized, self-guided, one-on-one law tutors. It also reports the Part II experiments on using a generative AI system, Claude 3.5 Sonnet, as a personalized one-on-one tutor to improve a novice learner’s performance on objective examinations in subjects the learner has never studied.

Although the topic of this study is learning the law, the results and overall approach to using an AI as a personalized learning tutor can be applied to many graduate and undergraduate programs in universities and other levels of education. The advancements in tutoring represented by generative AI systems have increased the pace of adoption of AI technologies to the point that GAI tools can play a significant role in academic support in law schools and universities. Generative AI tools can help a student learn and understand material better, more deeply, and notably faster than traditional means of reading, rereading, notetaking, and outlining. GAI tools, particularly Intelligent Tutoring Systems (ITS), adaptive learning platforms, and AI-augmented tutoring solutions, have shown promise in enhancing student engagement, improving learning outcomes, and providing tailored academic support. AI can explain, elaborate on, and summarize course material. It can write and administer formative assessments, and, if desired, it can write self-guided summative evaluations and grade them. AI can translate material into and from foreign languages with a fidelity to context, usage, and nuances of meaning not previously seen in machine learning or neural network translation services. AI also can visualize material using the tools of visual generative AI that literally paint pictures of the subjects and situations in the material that can overcome students’ literacy issues both in the native language of the communication and in the students’ own native languages.

Mateusz Jacek Płonka (Silesian U Technology) et al. have posted “Evaluating the Effectiveness of Document Splitters for Large Language Models in Legal Contexts” on SSRN. Here is the abstract:

The study explores the development and application of an advanced artificial intelligence-based system aimed at improving the efficiency and accuracy of legal document processing. Due to the high volume, specialised vocabulary, and complexity of legal texts, traditional document management techniques often prove inadequate and error-prone, creating significant challenges for legal practitioners. The proposed method leverages natural language processing and machine learning algorithms to automate key processes such as summarisation, analysis, search, and classification. By utilising vector embedding techniques, the system enables precise information retrieval from large legal document collections, while advanced summarisation methods generate concise and relevant summaries of extensive texts. The study employs a Retrieval-Augmented Generation approach, combining large language models (LLMs) with external knowledge bases to enhance the accuracy and contextual relevance of generated responses, addressing common issues such as hallucinations and outdated information in traditional LLMs. The research provides an in-depth analysis of the application of various text-splitting algorithms in the context of legal document databases. The findings highlight the characteristics of appropriate algorithms and offer recommendations on the conditions under which specific mechanisms should be employed.

Matthew Dylag (Dalhousie U) has posted “The Impact of Artificial Intelligence on Access to Justice: Predictive Analytics and the Legal Services Market” ((2025) 48:1 Dal LJ (forthcoming)) on SSRN. Here is the abstract:

This paper examines how developers of predictive analytics-a technology wherein artificial intelligence (AI) is being used to predict the future outcomes of legal disputes-position their product vis-à-vis access to justice. In particular, it examines how two companies market their software to better understand how this technology is being integrated into the legal services market and to comment on the software’s potential impact on access to justice. The first part of this paper reviews the access to justice landscape and examines existing critiques of AI supported technology from an access to justice perspective. The second part briefly outlines the scope and design of the study, while the third section reports and comments on the findings. Here, I note that these companies make several claims about their technology that, if true, could have a positive impact on access to justice. Specifically, both companies claim that their software will result in time savings, improved access to the law, improved legal clarity, and increased legal certainty. While these claims have some merit, their actual potential to improve access to justice is limited by the economic reality of the legal services market.

Jerrold Soh (Singapore Management University Law) has posted “NLP in the Legal World” on SSRN. Here is the abstract:

This talk situates the rising field of NLLP in the context of legal scholarship and emerging trends in legal AI practice and regulation. It centrally suggests that, as NLP’s domain of competence expands, it would have to undergo, and is in several ways already undergoing, a fundamental transformation we might refer to as “growing up”. In particular, to succeed in the legal world, NLP technology has to contend with three key aspects of adulthood: new attitudes, new consequences, and new responsibilities. Lawyers have gone from complete AI skepticism to actively exploring use cases. Encroaching into fields like medicine, law, and finance means technologists cannot avoid dealing with difficult questions around protecting life, liberty, and money. An entire new AI rulebook is currently being written by regulators and courts around the world. Against this backdrop, the talk examines how NLLP relates to existing inquiries in computational law, AI and Law, and computational/empirical legal studies and identifies opportunities for inter-field discourse. It concludes by identifying the unique role that NLLP researchers can play in the increasingly controversial (and seemingly, decreasingly scientific) global debate on the use and regulation of large language models.

Kirsten K. Davis (Stetson Law) has posted “A New Parlor is Open: Legal Writing Faculty Must Develop Scholarship on Generative AI and Legal Writing” (Stetson Law Review Forum 2024) on SSRN. Here is the abstract:

Generative artificial intelligence likely represents a paradigm shift in legal communication teaching, learning, and practice. What we know (so far) about generative AI suggests that law school legal writing courses will need to teach generative AI skills to be used as part of a hybrid human-generative AI legal writing process. Accordingly, legal writing faculty will need to understand how generative AI works, its implications for legal writing practices, and how to teach legal writers the knowledge and skills needed to use generative AI ethically and effectively in their work.

As a community of scholars, legal writing faculty should lead the inquiry into the connections between generative AI and legal writing products, processes, and practices. This is an exciting time; there are many unanswered questions to explore about the relationships between human writers and machine writing tools.

Rachelle Holmes Perkins (George Mason Law) has posted “AI Now” (Temple Law Review, Vol. 97, Forthcoming) on SSRN. Here is the abstract:

Legal scholars have made important explorations into the opportunities and challenges of generative artificial intelligence within legal education and the practice of law. This Article adds to this literature by directly addressing members of the legal academy. As a collective, law professors, who are responsible for cultivating the knowledge and skills of the next generation of lawyers, are too often adopting a laissez faire posture towards the advent of generative artificial intelligence.  In stark contrast to law practitioners and law students, law professors generally have displayed a lack of urgency in responding to the repercussions of this increasingly pervasive technology.

This Article contends that all law professors have an inescapable duty to understand generative artificial intelligence. This obligation stems from the pivotal role faculty play on three distinct but interconnected dimensions: pedagogy, scholarship, and governance. No law faculty are exempt from this mandate. All are entrusted with responsibilities that intersect with at least one, if not all three dimensions, whether they are teaching, research, clinical, or administrative faculty. It is also not dependent on whether professors are inclined, or disinclined, to integrate artificial intelligence into their own courses or scholarship. The urgency of the mandate derives from the critical and complex role law professors have in the development of lawyers and architecture of the legal field.