Le développement d’une IA explicable : entre principes éthiques généraux et mesures concrètes
Bibliographic record
Abstract
Résumé Les ingénieurs en IA ont besoin de directives applicables pour l’implémentation de principes éthiques dans leurs solutions technologiques. Mais comment y arriver ? Dans cet article, nous prenons le cas du développement de l’intelligence artificielle explicable (XIA) comme point de départ. Sur le plan des mesures concrètes devant être intégrées à l’IA pour la rendre explicable, nous remettons en question l’approche universaliste. Nous proposons une méthodologie normative pour évaluer les mesures de la XIA adaptées à des contextes spécifiques. Cette approche intègre l’éthique dans le développement de l’IA, offrant ainsi une méthode pragmatique pour les ingénieurs, régulateurs et chercheurs en éthique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".