The evolving AI regulation space
Bibliographic record
Abstract
As artificial intelligence (AI) technologies proliferate, the US federal government has oscillated on related executive orders, and no federal laws have addressed AI comprehensively. However, many states have passed legislations related to AI in the previous 5 years, and these laws are evolving and becoming more targeted, creating challenges and opportunities for government agencies. For this study, we compiled all passed and enacted legislations across the 50 US states in 2024 and examined them in terms of: domains; regulation of AI use in the public sector and industry; and novel topics and issues being addressed. In this preliminary analysis, we find that recent AI legislations are multiplying across US states, but unevenly. AI regulation across states continue to address various domains, including healthcare, education, and now also generative AI and AI-generated content. Legislations are expanding the role of the public sector in AI governance and AI policies, but issues of AI ethics, such as bias, are unevenly addressed across states, and few states have comprehensive AI governance frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".