Artificial Intelligence as a Judge: Myth of Impartiality and Legal Risks of Judicial Automation
Bibliographic record
Abstract
In recent decades, judicial systems in various countries have increasingly adopted artificial intelligence technologies, calling for an in-depth analysis of their potential and the risks they pose to fair justice. This study examines the prerequisites, prospects, and limitations of applying algorithms in judicial practice. The aim of the research is to uncover myths surrounding machine impartiality and to identify the key legal and ethical risks of automation. The objectives include comparing international experience, analyzing regulatory approaches in the United States, Canada, Europe, and Azerbaijan, and formulating conclusions on future development. The research employs an interdisciplinary approach, combining the study of legal norms, practical cases of implementation, theoretical concepts, and socio-legal consequences. The findings reveal the dual effect of algorithms: they accelerate data processing, improve the efficiency of document management, and assist in predicting case outcomes, while at the same time creating risks of reproducing biases, reducing transparency, and undermining public trust. Regional differences are notable: the United States focuses on predictive algorithms, Europe is developing strict regulatory frameworks, while Azerbaijan primarily applies supportive digital tools. Conclusion: Artificial intelligence can serve as a useful tool, but its use should remain within the framework of human rights protection, transparency, and the principle of fairness. The study recommends developing multi-level oversight that combines automation with responsible human participation
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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 | 0.059 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".