AI tools are increasingly suggested as solutions to assist public agencies with heavy workloads. In public defense, where a constitutional right to counsel meets the complexities of law, overwhelming caseloads and constrained resources, practitioners face especially taxing conditions. Yet, there is little evidence of how AI could meaningfully support defenders' day-to-day work. In partnership with the New Jersey Office of the Public Defender, we develop the NJ BriefBank, a retrieval tool which surfaces relevant appellate briefs to streamline legal research and writing. We show that existing legal retrieval benchmarks fail to transfer to public defense search, however adding domain knowledge improves retrieval quality. This includes query expansion with legal reasoning, domain-specific data and curated synthetic examples. To facilitate further research, we provide a taxonomy of realistic defender search queries and release a manually annotated public defense retrieval dataset. Together, our work offers starting points towards building practical, reliable retrieval AI tools for public defense, and towards more realistic legal retrieval benchmarks.

翻译：人工智能工具日益被提议作为协助公共机构应对繁重工作量的解决方案。在公设辩护领域——宪法赋予的律师辩护权与法律复杂性、超负荷案件量及有限资源相交织——从业者面临着尤为严峻的工作条件。然而，关于人工智能如何能切实支持辩护人日常工作的证据却十分有限。通过与新泽西州公设辩护人办公室合作，我们开发了NJ BriefBank检索工具，该工具能够呈现相关上诉状以简化法律研究与文书撰写工作。我们发现现有法律检索基准无法有效迁移至公设辩护检索场景，但引入领域知识可显著提升检索质量，具体包括：结合法律推理的查询扩展、领域特定数据以及精心构建的合成示例。为促进后续研究，我们提出了真实辩护人检索查询的分类体系，并发布了人工标注的公设辩护检索数据集。我们的工作共同为构建实用可靠、面向公设辩护的检索人工智能工具，以及建立更贴近实际的法律检索基准提供了起点。