Droit et soft ethics dans l’encadrement normatif de l’IA : une perspective pragmatiste
Bibliographic record
Abstract
Dans l’objectif de discuter des enjeux associés aux systèmes d’intelligence artificielle (SIA), les publications en éthique de l’IA se sont multipliées récemment. Bien que le droit et l’éthique œuvrent pour un but commun, soit celui de favoriser une utilisation de l’IA qui soit bénéfique et responsable, ces initiatives normatives sont distinctes et doivent être situées adéquatement l’une par rapport à l’autre. Dans le cadre de cet article, partant d’une perspective pragmatiste, nous proposons une réflexion sur le rôle normatif de ce que Luciano Floridi appelle la soft ethics par rapport au droit. Nous réfléchirons aux caractéristiques qu’elle devrait posséder pour jouer un rôle normatif effectif qui soit complémentaire au droit ainsi qu’aux relations internormatives entre éthique et droit dans la perspective du pluralisme normatif.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.072 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".