Bibliographic record
Abstract
The swift incorporation of artificial intelligence (AI) into legal systems across the globe is changing the way justice is administered and posing significant ethical, legal, and societal issues. From predictive risk assessment models like the UK's HART to case prioritization systems like Brazil's VICTOR, artificial intelligence (AI) solutions promise consistency and efficiency yet function as opaque decision-making entities with far-reaching effects. The structural, algorithmic, and institutional aspects of AI in courts are critically examined in this paper, with particular attention paid to issues of bias, accountability, transparency, and human rights. It examines how algorithmic governance interacts with legal norms, societal inequities, and procedural fairness through a comparative analysis of AI applications in Brazil, Singapore, Argentina, Colombia, India, and the United Kingdom. The study highlights the dangers of proxy-based discrimination, "black box" systems, and the responsibility gaps that come with automated decision-making. It delves deeper into ethical frameworks like the OECD AI guidelines, the Montreal Declaration, and the Asilomar Principles, putting forth a rightscentered paradigm for AI governance that upholds individual liberties, maintains judicial legitimacy, and lessens systemic unfairness. In the end, the paper makes the case that merging technical innovation with strong legal protections, democratic oversight, and open accountability procedures is necessary to achieve Algorithmic Justice.
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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 | 0.046 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".