The Concept of Culpability in Criminal Law and AI Systems
Bibliographic record
Abstract
This article focuses on the problems of the application of AI as a tool of crime from the perspective of the norms and principles of Criminal law. The article discusses the question of how the legal framework in the area of culpability determination could be applied to offenses committed with the use of AI. The article presents an analysis of the current state in the sphere of criminal law for both intentional and negligent offenses as well as a comparative analysis of these two forms of culpability. Part of the work is devoted to culpability in intentional crimes. Results of analysis in the paper demonstrate that the law-enforcer and the legislator should reconsider the approach to determining culpability in the case of the application of artificial intelligence systems for committing intentional crimes. As an artificial intelligence system, in some sense, has its own designed cognition and will, courts could not rely on the traditional concept of culpability in intentional crimes, where the intent is clearly determined in accordance with the actions of the criminal. Criminal negligence is reviewed in the article from the perspective of a developer’s criminal liability. The developer is considered as a person who may influence on and anticipate harm caused by AI system that he/she created. If product developers are free from any form of criminal liability for harm caused by their products, it would lead to highly negative social consequences. The situation when a person developing AI system has to take into consideration all potential harm caused by the product also has negative social consequences. The authors conclude that the balance between these two extremums should be found. The authors conclude that the current legal framework does not conform to the goal of a culpability determination for the crime where AI is a tool.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.076 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".