Les législations canadiennes et européennes face aux biais discriminatoires de l’intelligence artificielle
Bibliographic record
Abstract
L’essor des systèmes d’intelligence artificielle soulève d’importants défis juridiques, notamment en matière de discriminations algorithmiques. Cet article examine la capacité des cadres législatifs canadien et européen à répondre à ces biais algorithmiques. Il met en lumière les limites des classifications traditionnelles en droit anti-discrimination, particulièrement la distinction entre discrimination directe et indirecte, qui peine à saisir la complexité des décisions automatisées. Le corpus anti-discrimination n’est pas non plus le seul à pouvoir être invoqué en cas de biais algorithmiques : le tout nouveau règlement européen sur l’intelligence artificielle, détaillé mais rigide, ou le projet de loi canadien sur les données et l’intelligence artificielle, plus souple, font mention de ces biais discriminatoires. Malgré ces avancées, seule l’application de ces textes dira si ces régulations offriront une protection effective contre les discriminations algorithmiques.
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.
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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".