Key Takeaways
- Understanding local law can be overwhelming. Historically, reformers have hired large teams of researchers and lawyers to sift through federal and state codes, but that is particularly infeasible for thousands of cities, counties, and towns across the United States.
- Stanford HAI and RegLab researchers gathered 9,623 jurisdictions’ local laws (amounting to 3 billion words of code) and developed an LLM-assisted review pipeline to demonstrate the potential of AI for legal reform. Together, these laws govern in jurisdictions that are home to roughly 252 million Americans.
- We identify patently discriminatory laws. Shockingly, we document dozens of jurisdictions that formally still mandate racial segregation, impose poll taxes, and deny women the right to vote, practices that have long been held unconstitutional. A particularly prevalent category is unwarranted citizenship-based discrimination for occupational licenses.
- These methods empower lawyers, local governments, and citizens to understand and target reform efforts. LLM-assisted statutory surveys have the potential to update and make the legal code legible again.
Read the associated academic paper here.