利用大语言模型实现司法系统透明机制自动化：机遇与挑战
Automating Transparency Mechanisms in the Judicial System Using LLMs: Opportunities and Challenges
AI总结：
针对司法透明化需人工梳理大量案卷成本高的问题，提出利用LLMs的非结构化信息提取能力自动化透明流程，并探讨其在刑事陪审团遴选、住房驱逐案两类法庭程序中的应用机遇与挑战。
摘要：
为提升问责制而增强司法系统透明度，通常需要审计人员付出大量努力，他们必须仔细梳理大量杂乱的法律案件档案，以发现偏见和错误的模式。例如，备受瞩目的柯蒂斯·弗劳尔斯（Curtis Flowers）案调查，就花了7名记者整整一年时间，才收集到检察官在选择陪审团时存在种族偏见历史的证据。大语言模型（LLMs）具备实现这些透明流程自动化与规模化的潜力，尤其是其已被证实的从非结构化文档中提取信息的能力。我们探讨了在刑事审判陪审团遴选和住房驱逐案件这两个重要法庭程序中，使用LLMs提供透明度的机遇与挑战。
英文摘要：
Bringing more transparency to the judicial system for the purposes of increasing accountability often demands extensive effort from auditors who must meticulously sift through numerous disorganized legal case files to detect patterns of bias and errors. For example, the high-profile investigation into the Curtis Flowers case took seven reporters a full year to assemble evidence about the prosecutor's history of selecting racially biased juries. LLMs have the potential to automate and scale these transparency pipelines, especially given their demonstrated capabilities to extract information from unstructured documents. We discuss the opportunities and challenges of using LLMs to provide transparency in two important court processes: jury selection in criminal trials and housing eviction cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
