The Canadian AIDA and the EU AI Act: Will Sanity Prevail as they more closely align? – Part 1
Bibliographic record
Abstract
Abstract On June 16, 2022, the Canadian government introduced Bill C- 27, sponsored by the Minister of Innovation, Science and Industry, to update Canada’s federal privacy legal landscape. As earthshaking as that legislation was to the privacy regime in Canada, the impact of Bill C-27 was not limited to privacy regulation. Notably, Bill C-27 also introduced the Artificial Intelligence and Data Act (“AIDA”), which aims to introduce regulations in Canada regarding the design, development, and use of artificial intelligence (“AI”) systems. As we have previously written, while the AIDA is Canada’s first potential law aimed explicitly at regulating AI, it is in many cases influenced by the European Union’s then-proposed Regulation (EU) 2024/1689 (the “EU AI Act”) introduced on April 21, 2021. Time has since elapsed, and now AIDA - with the November 2023 introduction of new proposed (and substantive) amendments - and the July 2024 publication of the final EU Council-approved EU AI Act, have become increasingly aligned: good news for organizations in the AI industry. There do, however, continue to exist differences between the two items of legislation which can present traps for the unwary.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".