How and why governments are regulating AI: a comparison of legislative frameworks
Bibliographic record
Abstract
This paper examines different legislative approaches to regulate the production, deployment, and use of Artificial Intelligence (AI). Specifically, it compares how the European Union (EU), Canada, and the United States (US) craft their legislation to better understand the underlying policy goals that these governments seek to advance. The first part of the paper contextualizes the discussion by outlining the well documented dangers posed by unregulated AI systems as well as the technological narratives that have been constructed to craft public expectations of both government and industry. The second part of the paper explains the comparative exercise this paper undertakes and provides some procedural history on the current state of the laws being examined. The third section compares the approach of each piece of legislation in terms of scope, obligations, and compliance, while the final section discusses these findings. The paper notes that while the EU has established a comprehensive regulatory framework that prioritizes the protections of harms over other interests, the approach developed by both the US and Canada displays a reluctance to move away from existing self-regulatory models in preference of advancing commercial interests.
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 | 0.047 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".