Problem
Research questions and friction points this paper is trying to address.
local ordinances
legal AI
machine-readable corpus
U.S. municipal law
legal text accessibility
Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
🤖 AI Summary
This study addresses the fragmentation of U.S. local ordinances across numerous unstructured platforms and the consequent lack of a unified, scalable, machine-readable corpus that hinders legal AI research. To bridge this gap, the authors present LOCUS-v1, the first comprehensive corpus of local ordinances covering 9,239 municipal and county jurisdictions nationwide. They employ OCR techniques to process heterogeneous document formats and establish a standardized access layer for 2,309 counties. Leveraging this dataset, they develop classification and scoring models based on ModernBERT to analyze legislative dimensions such as transparency and paternalism. LOCUS-v1 constitutes the first large-scale, structured, metadata-rich dataset of local ordinances, substantially advancing foundational research and reproducibility in legal artificial intelligence.
📝 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://huggingface.co/datasets/LocalLaws/LOCUS-v1
Innovation
Methods, ideas, or system contributions that make the work stand out.
local ordinances
legal AI
machine-readable corpus
OCR processing
ModernBERT classifiers
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