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

TL;DR

The paper investigates how AI can realistically aid public defenders by conducting semi-structured interviews with fourteen practitioners. It identifies four adoption barriers (costs, office policies, confidentiality, and tool quality) and proposes a five-p pillar view of defender work, revealing evidence investigation as AI-ready and courtroom advocacy as less compatible. It prescribes safeguards such as mandatory human verification, vigilance against over-reliance, and preservation of human relationships, and advocates open-source, domain-specific datasets, and participatory design to advance equitable access to justice. The work contributes a nuanced task-level map for public defense AI, highlights vendor-related risks, and outlines a research agenda focused on open models and on-device deployment to strengthen confidentiality and cost-effectiveness. Overall, it pushes for responsible, task-aligned AI integration that supports defenders without eroding core professional values.

Abstract

Public defenders are asked to do more with less: representing clients deserving of adequate counsel while facing overwhelming caseloads and scarce resources. While artificial intelligence (AI) and large language models (LLMs) are promoted as tools to alleviate this burden, such proposals are detached from the lived realities of public defenders. This study addresses that gap through semi-structured interviews with fourteen practitioners across the United States to examine their experiences with AI, anticipated applications, and ethical concerns. We find that AI adoption is constrained by costs, restrictive office norms, confidentiality risks, and unsatisfactory tool quality. To clarify where AI can and cannot contribute, we propose a task-level map of public defense. Public defenders view AI as most useful for evidence investigation to analyze overwhelming amounts of digital records, with narrower roles in legal research & writing, and client communication. Courtroom representation and defense strategy are considered least compatible with AI assistance, as they depend on contextual judgment and trust. Public defenders emphasize safeguards for responsible use, including mandatory human verification, limits on overreliance, and the preservation of relational aspect of lawyering. Building on these findings, we outline a research agenda that promotes equitable access to justice by prioritizing open-source models, domain-specific datasets and evaluation, and participatory design that incorporates defenders' perspectives into system development.

Figures (3)

• Figure 1: Overview of our research process: we interviewed fourteen legal practitioners in public defense on their AI experience, workflow integration, and ethical concerns based on realistic cases. We applied qualitative analysis and literature synthesis, categorized public defense work into five pillars, examined normative and practical constraints, and designed principles for responsible AI adoption.
• Figure 2: Main barriers responsible for AI tool adoption in public defense work, grouped into three categories (confidentiality, output quality, and office policies). The darker the color of a box, the more often it has been mentioned.
• Figure 3: Ways AI tools can help in public defense work. Over half of the participants are optimistic about using AI tools to help with legal research and writing, as well as evidence investigation.