Doctoral Dissertation Research: Production and dissemination of legal knowledge in the era of generative AI

William Marsh Rice University, Houston TX

Investigators

Abstract

This doctoral dissertation project examines how the use of generative AI in the legal sector may transform legal expertise and reconfigure the positions of technical experts, legal professionals, and laypeople within the larger legal system. The novel data-driven AI solutions rely on identifying the stochastic patterns in existing legal data instead of encoded rules. For this reason, the conventions of AI-powered legal reasoning may deviate from human standards. Thus, large language models, such as ChatGPT, may significantly impact how specialized information is created and disseminated and how people access and make sense of it. While this project focuses on legal practice, its findings also contribute to developing a more profound, generalizable understating of the interactions between AI and established forms of expertise, informing legislative and policy-making efforts in the area of AI’s safety and its impact on various professions. Along with the training of graduate student, this project contributes to raising technology literacy around generative AI usage, especially in the context of the legal system. During a year of ethnographic fieldwork, the doctoral student examines how three groups of people interact with legal AI tools: (1) technical and legal experts developing legal AI; (2) legal professionals using AI at work; and (3) laypeople who may use AI while seeking legal advice. This allows the researcher to learn (1) what kinds of knowledge and expertise inform legal AI development and how the developers conceptualize successful legal AI tools; (2) what qualifies as legal work in the age of generative AI and who can perform it; and (3) how interactions with AI shape people’ expectations and strategies for engagement with the legal system. By answering these questions, this project contributes to the anthropological and socio-legal discussions around automation’s impact on labor and expertise, as well as technologies’ potential role in public governance. Simultaneously, this research contributes to developing the appropriate ethnographic approaches to conduct in-depth studies of AI systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.