Intelligence artificielle et manipulations des comportements de marché : l’évaluation ex ante dans l’arsenal du régulateur
Bibliographic record
Abstract
Le développement de l’économie numérique pose des problèmes inédits par leur ampleur en matière de possibles manipulations de marché et manipulations des choix des consommateurs. Des stratégies trompeuses et déloyales dans le champ du droit de la consommation peuvent coexister et se renforcer mutuellement, avec des infractions dans le champ de la concurrence, qu’il s’agisse de collusion algorithmique ou d’abus de position dominante. Face à la difficulté de détecter et sanctionner ces pratiques, l’effet dissuasif de la sanction, notamment pour des dommages possiblement irréversibles, est à questionner. À cette fin, cet article envisage les outils de supervision disponibles tant pour les autorités responsables de la supervision des marchés, les consommateurs ou les parties prenantes des entreprises concernées. Codes JEL : D18, K21, L86
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".