Штучний інтелект у судовій системі: осмислення правового поля
Bibliographic record
Abstract
The article examines the issue of using AI in the judicial system through an emphasis on the conceptual and categorical apparatus, legal regulation and the possibilities of applying AI in the judicial sphere. The author concludes that AI is actively being introduced into the legal sphere, but the lack of clear approved legal standards significantly levels this process. The uncertainty and ambiguity of understanding the language model and the features of application are noted, which causes problems in establishing a conceptual and categorical toolkit. It is motivated that the legal paradigm of legal regulation and the use of AI should be an ideological change in approach, where the potential of the studied system is used not only as a tool for automating or optimizing individual processes, but as a fundamental change in practices for decision-making, organizing management structures, forming policies and implementing state functions, including in the judicial sphere. The significant advantages of using AI for the judicial system are grouped: identifying systemic imperfections in judicial activity by analyzing a set of judicial and procedural documents in order to identify shortcomings in law enforcement; forming judicial practice; ensuring the integrity of representatives of the judicial corps, especially in the field of preventing corruption; assisting judges in the administration of justice; updating the comprehensive policy of reforming the judiciary by analyzing systemic gaps, obstacles, and social and political and legal reality in order to identify key factors of ineffectiveness of the current legal policy; ensuring proper public access to the court. It is stated that it is appropriate to use AI technologies to identify unfair practices of judges or court employees in making an unlawful, knowingly false court decision. Separate practices of using AI in judicial processes in Canada, Great Britain, and Italy are analyzed and the bifurcated nature of changes and the ambiguity of assessing opportunities are noted. The need to raise the awareness of the judiciary regarding the use of technology by using it as an auxiliary tool, and the need to verify the results of AI with human intelligence, is argued.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".