Pressing Issues of Unlawful Application of Artificial Intelligence
Bibliographic record
Abstract
The article discusses the problematic aspects of the implementation and application of artificial intelligence technology at the present stage of its development. The authors provide definitions of this technology, with its essential properties revealed based on their analysis. Criminological forecasting helps identify groups of crimes most likely to be committed through the use of artificial intelligence. The authors believe that at present there are not sufficient grounds for distancing ourselves from the issue of the subject of criminal liability in case of damage to public relations directly by the AI, but there are no circumstances due to which its resolution would not be delayed. The system of criminal law relations must be built based on scientifically developed provisions. The problems of criminal legal regulation, in terms of the impossibility of criminalizing and penalizing socially dangerous acts committed by artificial intelligence, are revealed. The legislator is asked to develop and adopt legal acts regulating the creation, operation, and use of artificial intelligence.
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 | 0.052 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".