arXiv:2606.19334v1 Announce Type: cross 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 -
The proliferation of AI systems capable of processing and understanding natural language makes the absence of structured legal data a critical bottleneck, prompting initiatives to create comprehensive corpora.
Access to a machine-readable corpus of local ordinances unlocks new possibilities for legal AI, research, and potentially government efficiency, impacting policy analysis and compliance at a foundational level.
Previously fragmented and inaccessible local legal data is now being systematically aggregated and structured, enabling large-scale computational analysis and application within AI models for the first time.
- · Legal AI developers
- · Legal researchers
- · Government agencies (local)
- · Compliance software providers
- · Legal data vendors (legacy)
- · Law firms relying on manual legal research
The creation of LOCUS provides a critical dataset for training and validating legal AI models focused on local regulations.
Improved access to local ordinances could lead to more efficient legal research, policy analysis, and potentially more equitable enforcement.
Highly granular legal AI could emerge, enabling automated compliance checks for businesses and individuals, or even AI tools for local legislative drafting.
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Read at arXiv cs.LG