Repenser la responsabilité administrative à l’ère de l’intelligence artificielle dans les services publics : vers un nouveau paradigme juridique
Bibliographic record
Abstract
L’intégration croissante de l’intelligence artificielle (IA) dans les services publics soulève des enjeux juridiques majeurs, notamment en matière de responsabilité administrative. Face à des décisions prises ou influencées par des systèmes algorithmiques souvent opaques et auto-apprenants, les mécanismes classiques de responsabilité pour faute ou sans faute - apparaissent inadaptés. L’article propose une relecture du cadre juridique existant à l’aune des exigences de transparence, de traçabilité et de redevabilité. Une analyse comparée des approches. Canadienne, Européenne et Américaine permet de mettre en lumière des solutions. alternatives. Enfin, des propositions normatives sont formulées pour instaurer une gouvernance algorithmique responsable, fondée sur un droit à l’explication, un registre public des algorithmes, et la création d’un régime spécifique de responsabilité algorithmique.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".