An Uncommon Task: Participatory Design in Legal AI

Abstract

Despite growing calls for participation in AI design, there are to date few empirical studies of what these processes look like and how they can be structured for meaningful engagement with domain experts. In this paper, we examine a notable yet understudied AI design process in the legal domain that took place over a decade ago, the impact of which still informs legal automation efforts today. Specifically, we examine the design and evaluation activities that took place from 2006 to 2011 within the TeXT Retrieval Conference's (TREC) Legal Track, a computational research venue hosted by the National Institute of Standards and Technologies. The Legal Track of TREC is notable in the history of AI research and practice because it relied on a range of participatory approaches to facilitate the design and evaluation of new computational techniques--in this case, for automating attorney document review for civil litigation matters. Drawing on archival research and interviews with coordinators of the Legal Track of TREC, our analysis reveals how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers and helped bridge the chasm between computational research and real-world, high-stakes litigation practice. In analyzing this case from the recent past, our aim is to empirically ground contemporary critiques of AI development and evaluation and the calls for greater participation as a means to address them.

References in corpus (2)

• Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
• Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation

Cited by in corpus (7)

• Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
• A Human-Centered Review of Algorithms in Decision-Making in Higher Education
• Non-Determinism and the Lawlessness of Machine Learning Code
• Exploring AI Problem Formulation with Children via Teachable Machines
• "This is not a data problem": Algorithms and Power in Public Higher Education in Canada
• Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling
• Adversarial Scrutiny of Evidentiary Statistical Software