U.S. State AI Policy Tracker

Bills Introduced per Year (2019–present)

Core AI bills per year (2019–present). Core AI bills are the validated tier of the classifier; adjacent-only bills are excluded from this dashboard after a precision validation study found 0% precision in that tier (see Methods tab for details).

Top States

Most active states in the most recent legislative session, ranked by core AI bill count.

Concepts matched in bills classified as Core AI. The catch-all "adjacent_tech" label is excluded; it marks adjacent-tier bills rather than a specific topic.

About The Tracker

The U.S. State AI Policy Tracker is a research tool developed by the Center for Analytics and Innovation with Data (CAID) at the University of Denver. It monitors AI-related legislation introduced across all 50 states, the District of Columbia, and U.S. territories, and cross-references those bills against the National Conference of State Legislatures (NCSL) AI legislation database. The tracker is intended for researchers, journalists, and policy practitioners who need a comprehensive, up-to-date view of where state AI policy is moving.

Step 1 - Data Collection

Legislative data is sourced from Plural Policy (formerly OpenStates), a nonpartisan organization that aggregates bill text, status, and metadata from all 50 state legislatures. We download bulk session files (structured JSON archives) for every available legislative session, resulting in more than 1.4 million collected bills.

Each session file contains bill identifiers, titles, full text where available, sponsor information, committee assignments, and action history. Raw files are stored locally and never modified after download.

Step 2 - Ingestion

Raw session files are parsed and loaded into a local DuckDB database. We use DuckDB because it is a fast analytical database that runs as a single file, no server required. The bills database is treated as append-only: once a bill is written, it is never updated or deleted. Re-running ingestion on the same session replaces only the records for that session.

Each bill record includes the bill identifier, title, full text (where available), session, state, and last-updated timestamp from Plural Policy.

Step 3 - AI Flagging

Bills are scanned against a curated keyword pattern library to identify AI-related legislation. The dashboard displays bills from the core tier only (see note below):

Matching is applied to bill titles and, where available, full bill text. The pattern library contains approximately 128 patterns across both tiers and is reviewed periodically to improve coverage and reduce false positives. Each matched bill records which specific concepts triggered the match.

Dashboard scope - core tier only: The tracker classifier originally flagged bills in both a core and an adjacent tier. A precision validation study (hand-coding of ~650 flagged bills) found that adjacent-only bills - those matching only broad adjacent patterns with no core-tier match - had 0% precision: all 146 coded adjacent-only bills were false positives. The adjacent-only pattern (adjacent_tech) matches common terms like data, bias, token, and temperature that appear throughout non-AI legislation. This dashboard therefore displays core-tier bills only (estimated precision ~79%, 95% CI: 73%–86%). A forthcoming methods paper reports the full validation results.

Step 4 - NCSL Validation

Flagged bills are cross-referenced against the National Conference of State Legislatures (NCSL) AI legislation tracking, drawn from five sources:

• The NCSL searchable AI Legislation Database (all years)
• Year-specific bill tables for 2025, 2024, and 2023
• A multi-year table covering 2019–2022

Together these sources provide approximately 2,600 unique state-bill combinations spanning 2019 to the present. Matching is performed by state and bill identifier, with normalization to handle common formatting differences (e.g., "SB 123" vs "S123"). Bills found in any NCSL source are marked NCSL matched in this tracker.

Step 5 - Bill Status

The Bill Browser displays legislative status information from two sources:

• NCSL status - for bills matched to an NCSL source, the legislative status reported by NCSL (e.g., Enacted, Pending, Failed - Adjourned, Vetoed) is shown as a color-coded label. This is the most reliable status signal available, as NCSL curates it manually.
• Latest legislative action - for all bills in the Plural data, the most recent recorded action and its date are shown in the expanded bill detail panel. This is free-form text sourced directly from state legislature feeds (e.g., "Referred to Committee on Labor" or "Approved by the Governor") and is not standardized across states.

A standardized, cross-state bill stage classification (Introduced, In Committee, Passed Chamber, Enacted, etc.) is not currently available from Plural's bulk data. The NCSL status field is the best available proxy for bills that have advanced in the legislative process.

Limitations

• Coverage depends on Plural Policy. If a state legislature is not fully covered by Plural Policy, bills from that state may be undercounted. Coverage is strongest for the largest and most active state legislatures.
• Full text is not always available. Some states publish only bill titles and identifiers; others publish full text. Matching against titles alone may miss bills that use AI-related language only in the body of the bill.
• Core-tier bills only. Adjacent-only bills are excluded from this dashboard. A hand-coded validation sample found 0% precision for adjacent-only matches — every coded bill in that tier was a false positive. Core-tier bills carry an estimated precision of ~79%. Bills that match both core and adjacent patterns (co-tier) are included and have an estimated precision of ~53%.
• NCSL coverage is not exhaustive. The NCSL database focuses on bills that have advanced in the legislative process. Bills that were introduced but failed to advance may be in this tracker but not in NCSL, and vice versa.
• Data currency. The tracker is updated periodically as new bulk data becomes available from Plural Policy and as the NCSL database is refreshed. The date of the most recent update is shown at the top of this page.

Citation and Contact

If you use this tracker in published research, please cite it as:

Center for Analytics and Innovation with Data (CAID), University of Denver. U.S. State AI Policy Tracker. https://du-caid.github.io/tracker/

For questions about methodology, data access, or collaboration, contact the CAID lab via the contact page.