Towards an approach to define transparency requirements for maritime collision avoidance
Bibliographic record
Abstract
This study discusses an approach to support human supervision of autonomous maritime collision avoidance systems by disclosing the system’s perceived information, internal reasoning, decisions, and planned actions as layers of transparency. Information requirements, identified through a cognitive task analysis, were structured using the information processing model by Parasuraman, Sheridan, and Wickens (2000). This model was contextualized to the maritime collision avoidance setting such that the information from the analysis could be structured into unique and distinct layers. A set of minimum information requirements was identified depicting the system’s decisions and planned action, supported by additional layers to reveal its internal reasoning. This approach aims at supporting humans in effectively supervising autonomous collision avoidance systems in their operational context by providing understandability and predictability about what the system is doing, why it is doing it, and what it will do next, i.e., transparency.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".