Informing Possible Futures for the use of Third-Party Audits in AI Regulations
Bibliographic record
Abstract
Canada’s proposed Artificial Intelligence and Data Act (AIDA) seeks to establish audit requirements for high-impact AI systems under certain circumstances and includes a proposed role for audits in assuring the actions, policies, and measures taken to manage the risks of AI impacts by entities engaged in regulated activities. At their most basic, audits are about checking that rules or expectations are being met by the target of the audit, with some level of confidence. The use of audits has expanded considerably in recent decades, and they have become a feature of many proposed and enacted regulatory approaches for governing the risks of negative AI impacts.
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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 | 0.203 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".