A Review of the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law
Bibliographic record
Abstract
The opening for signature of the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (hereinafter “the Convention”) in 2024 marks a symbolic event in the field of AI governance. The Convention sets a good example for the international community to prevent and govern risks to human rights, democracy, and the rule of law encountered in the process of AI governance. By analyzing the Convention's regulatory objects, basic positions, implementation methods, and approaches, this paper points out that the Convention itself does not create new types of human rights or obligations of human right; yet instead it relies on the existing human rights treaty framework to stipulate the basic principles that a series of AI activities shall abide by. The Convention has a set framework and inherent ambiguity. Compared to the “soft law” model of other related international documents, the Convention adopts a “hard law” model, yet its specific implementation still counts on domestic legislative, law enforcement, and judicial activities. Although its implementation effects remain to be seen, its “human-centred” regulatory approach, which covers a broad scope of protection for individual rights and interests and emphasizes the rights of specific groups, is worthy of reference.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".