The Development of the Legal Framework for Autonomous Shipping: Lessons Learned from a Regulation for a Driverless Car
Bibliographic record
Abstract
This article focuses on the regulation of maritime autonomous surface vessels from the perspective of international law of the sea. The article discusses on the possibility of developing a legal framework to regulate autonomous maritime navigation based on laws and regulation of autonomous driving of landed vehicles. The authors opine that existing legal framework does not conform to the goal of regulation of autonomous navigation. However, the regulation of autonomous car testing and exploitation could be imitated to design a new legal framework for autonomous shipping. Despite the divergent approaches, some principles remain in common particularly of cybersecurity and privacy. As computer systems are replacing the need of a master and crew for digitally managed ships, low level of cybersecurity implies an increase in risk of losing control over the vessel. The authors are of the opinion that that current legal acts, standards and their drafts do not pay necessary attention to the problem of cybersecurity of autonomous ships. Moreover, current legislations do not provide mechanisms of influence on behavior of shipowner and shipbuilder to make them apply the best measures. The similar situation is with privacy. Factually, an autonomous ship is a natural tool for surveillance, as to effectively navigate through the seas, it must collect and process information pertaining to navigational safety and other related matters. The question raises how this information has to be collected, kept, processed and deleted. Thus, the maritime community may consider adopting the approach on privacy from regulation for autonomous cars.
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".