Bibliographic record
Abstract
L’implantation des systèmes d’intelligence artificielle (SIA) dans le milieu de la santé permet d’énormes possibilités et avantages, mais elle s’accompagne aussi d’importants enjeux éthiques. Quantité de rapports visant à définir les principes moraux qui devraient orienter et baliser ces enjeux ont été publiés. Aujourd’hui, les appels se font de plus en plus entendre au sujet de la nécessité de passer de la théorie à la pratique en éthique de l’intelligence artificielle (IA) en organisation, notamment dans le milieu de la santé. Endossant une vision pluraliste et pragmatique de l’éthique, je suggère d’introduire l’approche de l’éthique de l’IA comme pratique, comme le practice turn a fait évoluer les théories en stratégie. Ce faisant, j’avance le concept de processus de construction et de partage de sens éthique des acteurs du milieu face au nouveau contexte qu’est l’implantation des SIA en organisation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".