Artificial Intelligence and Human Rights: An Analysis of the Council of Europe’s Framework Convention on Human Rights, Democracy and the Rule of Law
Bibliographic record
Abstract
This paper provides a legal analysis of the Council of Europe's Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law, which was adopted by the Council on 17 May 2024.This international convention seeks to complement the legal framework for AI governance, ensuring respect for human rights in public and private activities related to AI.Following a meeting of the foreign ministers of the Council of Europe's 46-member states during their annual session in Strasbourg, it was established with the participation of 11 non-EU countries, including the United States, Canada, and Japan.Its complementarity to existing international standards concerning human rights, democracy, and the rule of law makes it the first legally binding international text in the field of artificial intelligence.However, it does not regulate technology itself; rather, it aims to address any legal gaps that may arise from rapid technological advancement.Based on fundamental AI ethics principles, it obliges states to implement it by incorporating it into their national legislation.It will come into effect after ratification by five states, as stipulated in Article 30/3, but this has not yet been achieved despite the number of ratifying countries exceeding the required amount.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.018 | 0.014 |
| 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".