EDIA et systèmes d’intelligence artificielle au Canada : libres propos sur l’élaboration d’une future législation fédérale
Bibliographic record
Abstract
Les systèmes d’intelligence artificielle (SIA) sont en plein essor. Si leur utilisation procure des avantages indéniables, elle expose également à des risques. Dans le domaine du travail et de l’emploi, les SIA pourraient, entre autres, amplifier les biais, source de discrimination à l’endroit des groupes déjà marginalisés. Pour limiter ces risques, encadrer juridiquement les SIA est aujourd’hui une nécessité. Or, au Canada, le texte contenant la Loi sur l’intelligence artificielle et les données (LIAD) censée le faire est depuis mort au feuilleton . S’appuyant sur une démarche comparatiste, l’étude entend, premièrement, montrer que le dispositif juridique actuel du Canada est insuffisant pour contrer adéquatement les discriminations algorithmiques. L’article fait état, deuxièmement, d’éléments à prendre en compte lors de l’élaboration d’une nouvelle loi fédérale sur les SIA.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".