Штучний інтелект у досудовому розслідуванні кримінальних справ: окремі питання міжнародної практики
Bibliographic record
Abstract
It is indicated that the integration of artificial intelligence (AI) technologies into various aspects of public life opens up new horizons and at the same time creates serious challenges for the legal system, in particular in the field of criminal proceedings. Although the use of AI systems at the stage of pre-trial investigation significantly increases the efficiency and effectiveness of solving crimes, it also gives rise to a complex of complex issues of a legal, ethical and procedural nature. The article examines some aspects of the international practice of introducing artificial intelligence technologies into pre-trial investigation of criminal cases. The main approaches to legal regulation of the use of artificial intelligence systems in criminal proceedings of various countries, in particular the USA, Great Britain, Japan, Canada, France, the Netherlands, Singapore and Australia, are analyzed. Particular attention is paid to the analysis of US legislation, which has created a comprehensive system of legal regulation and control over the use of artificial intelligence in law enforcement activities. Key regulatory and legal acts are considered: the Electronic Communications Privacy Act, the Foreign Intelligence Surveillance Act and the USA PATRIOT Act, which establish the legal framework for the application of AI technologies in pre-trial investigation. Mechanisms of judicial, parliamentary and departmental control over the use of AI systems in criminal proceedings are studied. Considerable attention is paid to the ethical aspects of the implementation of artificial intelligence in law enforcement activities. The experience of different countries in creating specialized institutions and developing ethical codes of practice for the use of AI is analyzed. The main principles of the ethical application of AI technologies in pre-trial investigation are identified: transparency of the decision-making process, protection of privacy, ensuring accountability and building public trust. The results of the study identify a trend towards the formation of a comprehensive approach to regulating the use of AI, combining legal control mechanisms with ethical standards. The need to ensure a balance between increasing the efficiency of the investigation using AI technologies and protecting the rights of participants in criminal proceedings is substantiated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".