Les trois grands défis posés par la gouvernance de l’intelligence artificielle et de la transformation numérique
Bibliographic record
Abstract
Alors que la généralisation de l’emploi de l’intelligence artificielle s’opère dans la plupart des secteurs d’activité, les initiatives pour encadrer cette évolution, par la voie de l’éthique et du droit, se concrétisent. L’on pourrait être satisfait de la prise de conscience par les décideurs publics de l’impact transversal de cette technologie sur les individus et l’entière société, mais un certain nombre de grands défis paraissent avoir été notablement sous-estimés dans les débats ces dernières années. Cet article propose d’identifier trois séries d’actions qui devraient irriguer les travaux des régulateurs : déconstruire le mythe de la neutralité des technologies numériques, objectiver les capacités des systèmes d’« IA » et évaluer la soutenabilité environnementale d’une société numérique.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".