AI Acts in Focus: Comparative Insights from the European Union and Canada for India’s Policy Evolution
Bibliographic record
Abstract
This paper assesses and compares the European Union's 'Artificial Intelligence Act, 2024' with Canada's 'Artificial Intelligence and Data Act, 2022.'It investigates imperative components of both the AI Acts, like risk-oriented frameworks, ethical standards, innovation incentives, and compliance systems.Considering that India has emerged as a major force in artificial intelligence research and development, the study highlights the necessity of utilizing the legislative frameworks of other countries to develop a regulatory strategy that harmonizes with the socio-economic and technological needs of its citizens.The research covers a range of recommendations, including establishing AI sandboxes & risk-management systems, running community awareness campaigns, and enforcing resilient data protection legislation.Furthermore, it accentuates the significance of cooperation between governmental departments, academia, and business stakeholders to intensify innovation while maintaining responsibility.Such magnified understanding is anticipated to help India find a 'catalyzing' middle ground between the requirement for AI innovation and ethical and 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".