Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it