Bibliographic record
Abstract
Abstract This paper argues that robust human oversight in justice system deployment of artificial intelligence is not a mere contemporary regulatory response, but an intrinsic requirement deeply embedded within diverse global legal traditions. As AI becomes increasingly integrated into justice processes, the prevailing “human in the loop” paradigm must be critically examined, as it often proves insufficient in addressing algorithmic opacity, bias, and potential epistemic injustice. This article explores foundational principles of human-centric adjudication across international common law, civil law, Global South, and Indigenous legal traditions, highlighting the enduring importance of procedural fairness, judicial independence, and the duty to give reasons. This paper posits that “human in the loop” is not a modern accessory but a principle with deep, inherent roots in these traditions. To preserve justice’s foundational human-centric nature, this analysis argues for an evolution from a jurisdiction-specific “human in the loop” model to a globally informed governance model incorporating the “society in the loop” framework, as first proposed by Iyad Rahwan. Through a comparative analysis of judicial AI adoption and governance structures in Canada, the European Union, and the Global South, the article assesses how existing legal norms are challenged or upheld. It ultimately proposes an “intertraditionalist” approach to AI governance that acknowledges legal pluralism, aligning AI deployment with foundational principles to maintain judicial integrity and ensure epistemically just outcomes.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.045 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".