用LOCUS释放法律：美国的地方法规体系
Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
摘要
法律人工智能的发展越来越依赖于大规模获取权威法律文本。然而，美国法律中最重要的组成部分之一却几乎无法被现有的机器可读取数据库所包含——即地方法规。地方法规涉及分区规划、住房管理、企业许可、公共卫生、噪音控制、动物管理以及许多其他日常监管领域。但这些法规分散在那些专为人类浏览而设计的平台中，而非用于大规模研究的数据集中。我们推出了LOCUS——即美国的地区法规数据库——这是一个包含所有公开可用的地方和县级法规的综合性数据库及统一的访问层。该原始数据库可供研究人员使用，其中包含了几乎所有公开可用的地方和县级法规。该数据库包含了9,239个城市和县的相关法规。而较小的统一访问层则覆盖了美国3,144个县中最大的2,309个县，这些县占据了人口的大多数。我们使用OCR技术来处理各种文档格式，因为这些格式使得法律无法成为公共资源。我们还提供了包含相关元数据的数据集，以支持研究的可重复性、后续法律人工智能研究，以及机器可读取地方法规的逐步扩展。我们还训练了基于ModernBERT的分类器和评分器，以便从多个维度分析美国地方法规，比如法规的复杂性以及官僚主义程度等，这些方面之前并未被如此大规模地研究过。LOCUS-v1及其衍生模型可以在以下链接获取：https://hf-mirror.com/datasets/LocalLaws/LOCUS-v1
English Abstract
Progress in legal AI increasingly depends on access to authoritative legal text at scale. Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access. We introduce LOCUS - the Local Ordinance Corpus for the United States - a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. The raw corpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting raw corpus contains codes from 9,239 cities and counties. A smaller county-harmonized LOCUS access layer provides coverage for the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population. We use OCR to handle the myriad of document formats that have kept the law from being a public resource. We release the corpus with coverage metadata to support reproducibility, downstream legal AI research, and the incremental expansion of machine-readable access to local law. We train a collection of ModernBERT-based classifiers and scorers to facilitate analyzing U.S. local law among several dimensions, such as opacity and paternalism, that have not previously been studied at this scale. LOCUS-v1 and its derivative models are available at: https://hf-mirror.com/datasets/LocalLaws/LOCUS-v1