Steering the AI world: an exploratory comparison of AI Acts in the EU and Canada
Bibliographic record
Abstract
Introduction. As artificial intelligence (AI) continues to grow rapidly, governments are implementing legislative frameworks to address its risks and opportunities. This paper provides a comparative analysis of AI Acts in the European Union (EU) and Canada, focusing on two legislative efforts: the EU artificial intelligence act (EU AI Act) and Canada’s artificial intelligence and data act (AIDA). Method. A summative approach of qualitative content analysis was used to examine the scope, risk classification, and regulatory strategies employed in the EU AI Act and Canada’s AIDA. This study highlights similarities and differences in their approaches to managing AI’s societal impacts. Results. Both Acts provide positive directions and encourage responsible AI by addressing AI-related risks and opportunities. The analysis further explores the challenges, such as the definition of AI, enforcement mechanisms, and the inclusion of ethical considerations. Conclusion. By drawing on these cases, the paper illustrates how regulatory steering can ensure responsible AI development and deployment in different geopolitical contexts. This paper offers insights into the evolving nature of AI governance and contributes to the broader discourse on balancing innovation with societal safeguards.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".