Producer Liability for AI-Based Technologies in the European Union
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
The manufacturer's liability for defective products has remained almost unmodified since 1985 when Directive 85/374/EEC (=PLD) was enacted. Perhaps new technology based on artificial intelligence (=AI) could bring about a turning point in the regulation if concepts such as "product" and "defect" or aspects such as "grounds of liability", the so-called "development risks defense", and the "solidarity" are reconsidered. The Group of Experts on Liability and New Technologies (=NTF), in its “Liability for AI and other emerging digital technologies” Report, recommends, inter alia, the regulation of two different civil liability regimes: strict liability and fault-based liability. Thus, it will be necessary to determine precisely the cases to which these regimes apply and how to deal with “uncertain causation”. The alleviation of the victim’s burden of proof should be considered. From the various documents being published, it appears that the producer’s strict liability will remain as the main liability rule, but it will be combined -as the NFT suggests- in the case of the breach of a duty of care with a fault-based liability rule. This approach leaves some open questions, i.e., how to properly combine both grounds of liability in the domain of products that cause damages. In my view, the liability regime suggested by the NTF is far more complicated that the regime which distinguishes three types of defects that are often stressed: the defect of design, the defect of manufacturing, and the defect of information.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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