THE PROBLEMS OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE JUDICIAL SYSTEM OF RUSSIAN FEDERATION
Bibliographic record
Abstract
Background. The article analyzes the possibilities and limitations of integrating artificial intelligence into the judicial system of the Russian Federation using the example of procedural legislation. Purpose. The purpose of the study is to assess the compatibility of artificial intelligence technologies with the domestic legal system, identify legal conflicts and develop recommendations for adapting legislation. Methodology. During the research, the method of analysis, formal legal, comparative legal and hermeneutic approaches were used. The study used regulatory legal acts of the Russian Federation, the EU, Canada, the USA and China, scientific legal research and the legal press. Results. The main results of the study showed that the current procedural legislation of the Russian Federation is not adapted to the use of artificial intelligence. Experiments with "weak artificial intelligence" in contract manufacturing (Belgorod and Amur regions) have confirmed the need for legislative changes. Key problems were identified: algorithmic bias (using the COMPAS system as an example), lack of legal entity status for artificial intelligence, risks of cyber-attacks, contradiction to the principles of competitiveness, internal persuasion and independence of judges. Practical implications. Based on the analysis of foreign experience, recommendations are proposed: the consolidation of artificial intelligence as an auxiliary tool, the development of standards for the transparency of algorithms, the introduction of a risk-based approach, the training of judges and the creation of specialized legislation. The recommendations can also be used by the legislator when conducting research on the topic of the article. The importance of maintaining a balance between innovation and respect for the fundamental principles of law is emphasized. EDN: YRZZSB
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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".