Legal Analysis of Good Seamanship in Light of Autonomous Shipping
Bibliographic record
Abstract
This thesis examines aspects of collision liability and navigational rights and freedoms of autonomous vessels. The thesis starts out with an introductory chapter which defines some terms and gives a short introduction to the field of regulatory maritime law. In the introductory chapter, the term “autonomous ship”, is defined as a “a ship which, to a varying degree, can operate independent of human interaction”. This definition was chosen because it was used by the International Maritime Organization (an agency of the UN specialized in maritime issues). In the language of the IMO, autonomous vessels are called “Maritime Autonomous Surface Ship” and are abbreviated “MASS”. This abbreviation is used throughout this thesis. In the same chapter, it is pointed out that the right to freely sail and navigate the world’s oceans is given to water-crafts that can be defined as “ships / vessels” in the terminology of the United Nations Convention on the Law of the Sea (UNCLOS). The question is then asked if MASS could fit into this definition. This question is answered affirmatively. In the next chapter, the question is asked if MASS can live up to the obligation of what is called good seamanship, in rule 2 (a) of the International Regulations for Preventing Collisions at sea (COLREGs). It is found that this question cannot be answered conclusively, because it contains too many aspects. Therefore the discussion moves on to the obligation to keep a proper look-out (listed in COLREGs rule 5), which is a part of this obligation. The question of proper look-out is discussed based on doctrine and case law from the United Kingdom, USA and Canada. It is concluded that MASS operating completely autonomously, without any human oversight, could not live up to this requirement, but that remotely controlled vessels without seafarers on-board could, provided that the technology of the ship was sufficient.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".