Le rôle de l’éthique dans la mise en place d’une certification pour l’utilisation d’algorithmes dans le système juridique
Bibliographic record
Abstract
En règle générale, la prise de décision algorithmique peut influencer les individus sur plusieurs points. Tout d’abord, une problématique de transparence se pose, mais également de contrôle, dans la mesure où l’apprentissage machine lié à la puissance de calcul complexifient la compréhension du processus de raisonnement ayant mené à une décision. De la même manière, cela empêche l’identification et la résolution de potentiels problèmes éthiques liés à la conception et au fonctionnement de l’algorithme. À ce titre émergent des réflexions portant sur la mise en place d’une charte éthique, mais également d’une certification. La certification n’étant pas obligatoire, per se, nous verrons comment l’éthique pourrait compléter le rôle du droit en permettant de l’encourager.
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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 | 0.078 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".