Bibliographic record
Abstract
With the advancement of robotics and artificial intelligence, the nature of risks evolves in a way that makes them harder to foresee and mitigate against. While specific risks cannot be as easily foreseen as before, general risks can still be identified and mitigated against. This mitigation may gain from being done at both technical and non-technical levels. The way we address that changing nature of risks matters for generating and maintaining trust in the technology, and for setting the conditions for the widest possible societal deployment of the technology. This is relevant to all the actors in the robotics industry, and even for actors in neighbouring technological industries (IoT, AI, etc.) as public trust erosion in one technology or application would affect all other contemporary technologies. Current standards are not tackling this changing nature of risks, and are more generally unsuited to the current and near future technology. Compliance to those standards, or even to more precise and updated standards might not shield producers from damage claims in case of accidents. We cannot wait for public policymakers to find answers to those challenges and must engage in a self-regulation effort. This white paper thus attempts to explore the way engineers and lawyers deal with new types of risks, and tries to clarify the way lawyers and judges are likely to react when having to deal with accidents and other legal issues related to developmental technologies
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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.039 |
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".