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
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.
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 | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.034 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".