From Kafka’s Courts to AI Tribunals: Bureaucratic Alienation and the Future of Justice
Bibliographic record
Abstract
The Trial furnishes a diagnostic lens—opacity (epistemic), arbitrariness (normative), alienation (experiential)—for assessing AI-mediated governance in law and administration. Using literary hermeneutics, critical legal theory, and normative jurisprudence, the article maps this grid onto three emblematic systems—COMPAS (US), Chinook (Canada), and China’s social credit—showing how “black-box” procedures displace judgment, entrench structural bias, and erode due process. It evaluates partial remedies in the EU AI Act, GDPR, and UNESCO’s AI Ethics Recommendation, and specifies safeguards centered on transparency, contestability, accountability, and dignity (TCAD). The article concludes by flagging risks from large language models—fabricated citations, provenance-free drafting, and bias propagation—and by outlining TCAD-aligned controls to preserve agency and legal recognition in the age of 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.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".