DETECTING HUMAN RIGHTS VIOLATIONS THROUGH AI AND THE ROLE OF CONSTITUTIONAL COURTS
Bibliographic record
Abstract
This study examines the transformative role of artificial intelligence (AI) in detecting human rights violations and the evolving responsibilities of constitutional courts in safeguarding fundamental rights in the digital era. With the advent of technologies such as satellite imagery, facial recognition, and natural language processing, AI has become a vital tool for identifying and documenting abuses in regions affected by conflict, authoritarian governance, or limited accessibility. While international organizations and civil society actors increasingly rely on AI for proactive rights monitoring, these technologies raise significant legal and ethical concerns. Key challenges addressed include the admissibility and transparency of AI-generated evidence, the risks posed by algorithmic bias and false positives, and the difficulty of attributing responsibility in complex AI ecosystems. The study demonstrates how these issues strain traditional legal doctrines, particularly in constitutional adjudication where due process, human dignity, and legal certainty are central. Through a comparative lens focusing on Canada, the United Kingdom, and Japan, the paper explores how courts respond to AI-related disputes and adapt judicial review to account for opaque algorithmic systems. The role of constitutional courts is emphasized as a cornerstone in shaping rights-based governance frameworks for AI. Courts are urged to develop principles such as explainability, algorithmic accountability, and shared liability to ensure that automated systems align with democratic values. Institutional reforms—including technical advisory panels, judicial training, and interdisciplinary education—are recommended to enhance legal capacity in addressing AI’s complexities. Ultimately, the paper argues for a multidimensional approach integrating legal theory, technological literacy, and ethical oversight. Such an approach is essential to ensuring that AI strengthens, rather than undermines, constitutional protections and the broader commitment to justice, transparency, and human dignity in the digital age.
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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 | 0.067 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| 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".