Integrating Ethical Principles and Human Rights Based Approach in the EU Artificial Intelligence Act and the Council of Europe Convention on Artificial Intelligence: Interplay of Ethics and Law in the AI Regulation Debate
Why this work is in the frame
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Bibliographic record
Abstract
The current article is dedicated to the analysis of the ways how ethical principles have been translated into European artificial intelligence (AI) regulation. The authors explore their role in the implementation and enforcement of the AI regulation. By integrating diverse methods acquired from humanities, legal sciences, and philosophy, the current study strives to gain profound insights into the challenges and different approaches of regulating AI. First, the authors explore the interplay of law and ethics in the AI debate, describing it as a binary approach. They argue that ethical principles, if perceived as a moral philosophy deeply rooted in foundational values (so-called multi-dimensional approach), can provide valuable guidance in implementation of AI regulation. Further, the authors analyse the ways how ethical principles have been taken up by the EU Artificial Intelligence Act (AI Act) and the Council of Europe’s Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (Convention on AI or Convention). It focuses on the trade-offs made in this process, different approaches taken by the EU and the CoE to regulate AI and critically reflects current challenges in this process. The AI Act applies a risk-based approach that requires balancing various ethical principles, fundamental rights, values and interests, e.g. the development and uptake of AI. In turn, the CoE’s Convention on AI adopts a rights-based approach that has the potential to make a substantial difference by ensuring that AI systems are developed and used during their entire lifecycle in ways that protect fundamental rights, the rule of law, democracy, and social well-being. The article demonstrates how ethical principles and human rights-based approach can guide the development and implementation of the AI regulatory framework.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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