The Canadian Artificial Intelligence and Data Act and the EU AI Act: Will Sanity Prevail as they more closely align? – Part 2 — Changes to both Acts bring them closer together... but not too close
Bibliographic record
Abstract
Abstract Part 1 of this paper (Beardwood, CRi 2024, 97) provided an update on the progress of AIDA and the EU AI Act (I), outlined a summary roadmap of the base similarities and differences between the two items of legislation (II), reviewed the objectives of AIDA in contrast to the EU AI Act (III), compared their respective jurisdictional scope (IV), reviewed their respective definitions of AI systems (V), outlined new definitions/concepts which have been introduced into the legislation (VI), outlined the extent to which there are exclusions for the public sector (VII) and for research (VIII), and provided an overview of their respective risk-based approaches (IX). This Part 2 compares in detail the obligations for High-Impact Systems and General-Purpose Systems (AIDA) (X), and for High-Risk AI Systems and General-Purpose AI Systems (EU AI Act) (XI), and finally reviews the penalties and offences for noncompliance imposed by AIDA and EU AI Act (XII) before concluding (XIII).
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".