THE PROBLEMS OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE JUDICIAL SYSTEM OF RUSSIAN FEDERATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it