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

2025-10-26

AI Summary

This study investigates the practical challenges and pathways for leveraging AI to enhance access to justice amid chronic resource constraints facing public defenders. Method: Drawing on semi-structured interviews with 14 U.S. public defenders and qualitative thematic analysis, the study develops the first AI task-intervention map tailored to criminal defense contexts. Contribution/Results: It identifies evidence analysis as a high-potential application domain, while highlighting significant limitations in legal research and client communication. Key adoption barriers include cost, confidentiality risks, tool reliability, and office-level procedural norms—underscoring the irreplaceability of contextual judgment and relational work. The study proposes an innovative “task-level application framework” and a participatory design paradigm, accompanied by a technical roadmap featuring open-source models, domain-specific datasets, and task-oriented evaluation metrics. It further establishes governance principles centered on human oversight and professional autonomy.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• AI adoption barriers in public defense include costs and confidentiality risks
• Mapping where AI can assist with evidence analysis versus courtroom strategy
• Developing responsible AI safeguards through participatory design with defenders

Innovation

• AI analyzes digital evidence for public defense
• AI assists legal research, writing, and communication
• Human verification required for AI legal applications