Where Does the Novel Legal Framework for AI in Canada Stand against the Emerging Trend of Online Test Proctoring?
Bibliographic record
Abstract
Academic surveillance can be considered as an emerging field of “capitalism surveillance” (Zuboff) pertaining to the dominance of a few companies in the surveillance field. Online proctoring software represent a variety of tools often based on artificial intelligence, such as Respondus Monitor, Proctorio, ProctorU, ProctorExam, Examity, ProctorTrack. While these tools generate legal issues of socio-economic discrimination and privacy, most of Canadian universities have used them during the pandemic and sometimes before that. This paper considers the risks generated by AI tools for exam monitoring and the Canadian legal framework on data protection legislation, as well as on Artificial Intelligence (Bill C-27 – part 3: Artificial Intelligence and Data Act) in comparison with the European Commission&s;s proposal of regulation on AI (AI act). We make recommendations for the Canadian legislator to improve Bill C-27.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".