Establishing Frameworks for the Responsible Use of Artificial Intelligence in the Ukrainian Judiciary Based on Foreign Experience
Bibliographic record
Abstract
The adoption of new technologies by judges and court users presents various challenges and unpredictable risks, particularly concerning human rights and adherence to established standards of the administration of justice. Therefore, special frameworks should be set to regulate the use of artificial intelligence in the judiciary. These frameworks are expected to create tentative «safety rules» that govern the responsible utilization of artificial intelligence, promoting a more cautious approach. In this article, the author aims to summarize recommendations for Ukrainian judges and other court officials based on international practices. Special emphasis is placed on the development of «red lines», which are critical boundaries that, if crossed, could result in discrimination, violations of confidentiality and privacy, and inaccuracies in output data. The article focuses on adopted recommended acts for establishing the frameworks for using artificial intelligence in judicial proceedings. The author reviews the guidelines provided by the highest judicial authorities in countries such as Australia, Canada, Hong Kong, New Zealand, Singapore, and the United Kingdom. This comparison aims to highlight the necessity of developing similar guidelines for Ukrainian judges and other court officials, particularly after the Code of Judicial Ethics introduced a provision regarding the use of artificial intelligence. One of the essential safeguards for the responsible use of artificial intelligence in the Ukrainian judiciary is the accountability taken by judges and, in some cases, the parties involved in the proceedingsto ensure that advanced technologies support the objectives of the judicial system. Safe usage is achievable only when there is adequate awareness of artificial intelligence's capabilities and risks, as well as a sufficient level of digital literacy. The findings of the study highlight «safety rules» that, if adhered to, will help ensure that emerging technologies positively contribute to upholding the principles of justice. Key words: artificial intelligence, generative artificial intelligence, artificial intelligence in judicial proceedings, judge's use of AI technologies, Code of Judicial Ethics, Ukrainian judiciary, responsible use of artificial intelligence, Supreme Court of the Republic of Singapore, Federal Court of Canada
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".