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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it