Regulating the unregulated-legal reactions to the development of artificial intelligence
Bibliographic record
Abstract
The exponential growth of artificial intelligence (AI) has reshaped world economic, legal, and social structures, posing critical questions on how to control its deployment while protecting human rights. This paper studies legal responses to AI in key jurisdictions, the United States, European Union, China, and Canada, and their divergent regulatory philosophies. The EU employs a horizontal, rights-based approach based on ethics, data protection, and the "Brussels effect" that seeks to export its regime to the world. The United States follows a decentralized, market-centred regime combining federal guidance with various state-level experiments. China embraces a dirigiste approach with a focus on government control and data regulation via the PIPL, DSL, and CSL, with AI incorporated into judicial and administration proceedings. Canada, while circumspect, tests “regulatory sandboxes” and disclosure policies by courts to harmonize innovation with openness. Comparative studies show decentralized world governance and dangers of legal incompatibilities, imbalance in ethics, and digital disenfranchisement. The paper suggests the adoption of international standards on transparency, accountability, and fairness in algorithms; increased public-private partnerships; and capacity-building programs to make AI integration unbiased. Finally, the paper concludes that regulation of AI must transcend national borders, creating a consistent legal framework that harmonizes innovation with inherent rights, forestalls abuse, and fosters sustainable digital development. The paper is convinced that proactive, coordinated effort at a world level is the only way that AI can become a force for human progress instead of a dispenser of inequality and control. Keywords: artificial intelligence, regulation, legal frameworks, data protection, digital governance, ethical AI, human rights, accountability, transparency, European Union, United States, China, Canada.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".