ImmigrationReason：面向法律推理研究的美国移民上诉结构化数据集
ImmigrationReason: A Structured Dataset of U.S. Immigration Appeals for Legal Reasoning Research
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究推出ImmigrationReason大规模结构化数据集，涵盖美国移民上诉裁决相关数据，可支撑法律推理领域的结果预测、错误分析及高风险监管智能体设计等研究方向。
AI中文摘要：
大多数法律自然语言处理（NLP）资源均取自联邦判例法，且侧重粗分类，而占政府决策绝大多数的行政裁决领域基本未被涉及。我们推出ImmigrationReason，这是一个大规模结构化数据集，源自美国公民及移民服务局（USCIS）行政上诉办公室（AAO）2005年至2026年间的12375项非先例裁决。每条记录涵盖适用法律框架、基于五类标签的各标准证据充分性判定结果、裁决者批评的逐字引述、所有引用文献及最终处置结果，以及由Claude转录的高质量源文本。提取质量通过三轮流程验证，该流程结合两种独立模态并使用Opus 4.7的比较提示进行判定，且由领域专家对500条记录样本进行了核查。该数据集记录了近9000个AAO认定的法律错误逐字实例，涵盖自然法律制度转变（2016年Dhanasar规则变更），覆盖21年的裁决数据。我们对该数据集进行了详细分析，并概述了它所能支撑的研究方向，涵盖结果预测、裁决者错误分析以及高风险监管领域的智能体设计等。
英文摘要：
Most legal NLP resources draw from federal case law and focus on coarse classification, leaving administrative adjudication, where the vast majority of government decisions occur, essentially unaddressed. We introduce ImmigrationReason, a large-scale structured dataset derived from 12,375 non-precedent decisions of the U.S. Citizenship and Immigration Services (USCIS) Administrative Appeals Office (AAO) spanning 2005 to 2026. Each record captures the applicable legal framework, per-criterion evidence-sufficiency findings under a five-category label, verbatim adjudicator-criticism quotes, all citations, and final dispositions, alongside high-quality Claude-transcribed source text. Extraction quality is validated through a three-pass pipeline combining two independent modalities with comparison-prompt adjudication by Opus 4.7, and verified by domain experts on a 500-record sample. The dataset documents nearly 9,000 verbatim instances of AAO-identified legal errors, spans a natural legal-regime transition (the 2016 Dhanasar rule change), and covers 21 years of adjudication. We analyze the dataset in detail and outline research directions it enables, from outcome prediction and adjudicator-error analysis to agent design for high-stakes regulatory domains.
