How Can AI Augment Access to Justice? Public Defenders' Perspectives on AI Adoption

TL;DR

This study explores how AI can support public defenders by identifying tasks where AI is most effective, highlighting ethical considerations, and proposing a research agenda to improve access to justice.

Contribution

It provides a detailed task-level map of public defense work, revealing where AI can and cannot assist, based on interviews with practitioners.

Findings

Evidence investigation is the most AI-suitable task.

AI has limited roles in courtroom representation and defense strategy.

Adoption barriers include costs, norms, confidentiality, and tool quality.

Abstract

Public defenders are asked to do more with less: representing clients deserving of adequate counsel while facing overwhelming caseloads and scarce resources. Although artificial intelligence (AI) is often promoted as a means of relieving administrative and cognitive burdens, legal AI research rarely engages with the everyday realities of public defense work. Drawing on in-depth, semi-structured interviews with 17 public defense professionals across the United States, we identify work-intensive tasks most amenable to AI assistance and the ethical constraints involved in legal representation. We develop a comprehensive task-level map of public defense work, dividing it into five pillars to clarify where AI can and cannot contribute: evidence investigation, legal research & writing, client communication & support, courtroom representation, and defense strategies. Interviewees consistently identified evidence investigation, such as reviewing large volumes of digital records, as the area with the greatest potential for AI support. AI was viewed as having more limited roles in legal research and client communication, and as least compatible with courtroom representation and defense strategy. We find that AI adoption is constrained by costs, restrictive office norms, confidentiality risks, and unsatisfactory tool quality. Our interviewees emphasize safeguards for responsible use, including mandatory human verification, limits on over-reliance, and the preservation of relational aspects of lawyering. Building on these findings, we outline a research agenda that promotes equitable access to justice by prioritizing open science, building domain-specific datasets and evaluation, and incorporating frontline practitioners' perspectives into system development.