The Canadian AIDA and the EU AI Act: Will Sanity Prevail as they more closely align? – Part 1
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 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.
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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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