The EU AI Act’s Alignment within the European Union’s Regulatory Framework on Artificial Intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary The European Union (EU) Artificial Intelligence (AI) Act is the first horizontal regulation on AI, aiming to regulate the development, placement on the market, and use of AI systems in the EU. The initial proposal was published by the European Commission (EC) in April 2021, and after an intensive three-year period of discussions, revisions, and negotiations, on December 9, 2023, a provisional agreement was reached on the final text. This marked the culmination of a series of ethical policy and legislative foundations that have created a broad and highly influential regulatory framework on AI in the EU. However, the consistency of the final draft within this established institutional environment on AI merits a close examination. This paper studies the AI Act text and its alignment within this framework. It will use the partial institutional analysis method to map the regulatory landscape, identify the most important sources within the said scope, and critically assess their consistency.
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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 | 0.004 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it