Regulating the machine: An exploratory study of US state legislations addressing Artificial Intelligence, 2019-2023
Bibliographic record
Abstract
Artificial Intelligence (AI) poses transformative and disruptive challenges for democracy, for policy makers, and for government agencies. While various policy initiatives around the world seek to regulate AI, in the United States (US) federal government there is no sign of a comprehensive AI law, and few legal measures to enable or restrict AI have been proposed and passed. However, states across the US are active in attempting to address issues related to AI and have proposed hundreds of legislations related to AI in the past few years. In this paper, we examined what these legislations have sought to accomplish in relation to AI, and the potential impacts for the public in general and for public administration in particular. From a preliminary and descriptive analysis of all US state legislations related to AI passed from 2019 to 2023, we show how these legislations are addressing AI in terms of: (1) the types of legislations adopted or enacted; (2) the definitions of AI and associated technologies given; (3) the sectors and domains principally addressed in AI legislations; (4) the private sector and government actions directed by the legislations; and (5) how ethical and economic considerations are addressed. We generally found a lack of definition of AI, and associated technologies mentioned are rarely specific. Many of the laws create commissions or task forces to study AI, addressing the various practical and ethical issues related to AI. Legislations have created some regulations and support for industry, and have directed government agencies to identify existing AI capabilities and how AI may be employed in their agencies and jurisdictions. Considerable emphasis has been placed on issues of bias and discrimination, as well education and economic investment in AI, although unevenly distributed across states. We summarize and discuss these results in relation to existing literature and make some recommendations on how state legislatures may better address AI in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".