Legal, Ethical and Industry Issues and Developments regarding Maritime Autonomous Surface Ships
Bibliographic record
Abstract
Abstract This article presents selected legal, industry, and ethical issues raised by autonomous vessels ( MASS ). The issues selected are deemed important on the basis of the work done at the International Maritime Organization level, literature and/or the practice of MASS . Such issues revolve around the defi-nition of a vessel, seaworthiness, the presence of a crew and master on board a vessel, the Polar Code and MASS (legal concerns) and the presence of smart ports (industry concerns). Another aspect of MASS discussed are the ethical concerns they raise, such as invasion of privacy, lack of trans-parency, non-discrimination, and accountability due to the technology they use. The issues treated are not exhaustive in number or content and are not specific to a geographical region. However, since they are of interest to Canada, Canadian laws and/or case law or initiatives are often touched upon. The objective is to give to the reader a glimpse into the highly diver-sified regulatory (legal and ethical) and industry issues raised by MASS and to make some suggestions about how to deal with them in the future.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".