How Are Canadians Regulating Artificial Intelligence for Healthcare? A Brief Analysis of the Current Legal Directions, Challenges and Deficiencies
Bibliographic record
Abstract
Effective regulations can ensure a minimum level of performance from artificial intelligence (AI) systems. Canadian regulators face two major categories of challenges. First, the AI-specific challenges stem from the unpredictable developments, use, evidence, and acceptable ethical trade-offs around AI systems. These uncertainties can drive the need for flexible definitions of risk, evidentiary threshold, change plan, and post hoc determination of ethical trade-off. These regulatory flexibilities could neglect impactful AI systems, allow regulatory capture, and undermine public oversight. Second, the jurisdictional challenges obfuscate the scope of products, regulatory boundaries, and division of power across regulations. Clarifying regulatory definitions, the responsibilities of professional bodies, and the need for provincial and territorial legislations may help. However, the lack of reason to believe that regulators have clear motivation and capacity to meaningfully protect patient health is worrisome.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".