La saisie de données informatiques en droit criminel canadien
Bibliographic record
Abstract
Les implications de l’intelligence artificielle sont complexes lorsqu’il est question de responsabilité criminelle. En effet, même avec un exemple simple tel que les voitures autonomes, il n’est pas évident de déterminer comment le droit criminel pourrait répondre aux problèmes soulevés par ces nouvelles technologies. Dans ce cas précis, serait-ce l’entreprise fabriquant la voiture qui serait respon- sable en cas de conduite dangereuse ou de délit de fuite, l’individu se trouvant derrière le volant au moment des faits ou plutôt le véhicule lui-même ? Bien que pouvant sembler futuristes ou farfelues, ces questions se retrouveront devant les tribunaux probablement bien plus tôt que ce que nous pouvons penser.\nLes principes généraux applicables à la responsabilité crimi- nelle peuvent nous aider à répondre à ces questions. En analysant ceux-ci, nous tenterons d’élaborer quelques pistes de réflexion en lien avec cette problématique.\nThe implications of artificial intelligence are complex when it comes to criminal liability. Indeed, even with a simple example such as autonomous cars, it is not clear how criminal law can respond to the problems raised by these new technologies. In this case, would it be the car manufacturer that would be responsible in case of dangerous driving or hit-and-run, the individual behind the wheel at the time of the event, or rather the car itself? Although they may seem futuristic or far-fetched, these issues will likely end up in court much sooner than we actually think.\nThe general principles applicable to criminal liability can help us answer these questions. By analyzing them, we will try to develop some prospective solutions on this issue.
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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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".