Producer Liability for AI-Based Technologies in the European Union
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.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".