AI tools are 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 retrieval benchmarks fail to transfer to real public defense research, 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 release a taxonomy of realistic defender search queries and a manually annotated evaluation dataset for public defense retrieval. This benchmark is highly correlated with a proprietary retrieval dataset annotated by experienced public defenders. Our work improves on the status quo of realistic legal retrieval benchmarking and illustrates one approach to applying AI in a real-world public interest setting.

翻译：人工智能工具被提出作为辅助公共机构应对繁重工作量的解决方案。在公设辩护领域——当宪法赋予的律师权与法律复杂性、过量的案件负担以及资源受限情况相遇时——从业人员面临着尤为严峻的工作条件。然而，关于人工智能如何切实支持辩护人员日常工作的证据仍然匮乏。我们与新泽西州公设辩护人办公室合作，开发了NJ BriefBank检索工具，该工具能够检索相关上诉状，以简化法律研究与写作。我们发现，现有的检索基准无法迁移至真实的公设辩护研究场景，但通过引入领域知识可提升检索质量。这包括结合法律推理的查询扩展、领域特定数据以及精心策划的合成示例。为促进进一步研究，我们发布了一套面向公设辩护检索的现实辩护人查询分类体系及一份人工标注的评估数据集。该基准与经验丰富的公设辩护人标注的专有检索数据集高度相关。本研究改进了现实法律检索基准的现有水平，并展示了在真实公共利益场景中应用人工智能的一种可行路径。