From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support

Trust in maritime AI requires more than accuracy: The paper emphasizes that in high-stakes maritime environments, system performance alone is insufficient—transparency and interpretability are equally critical for building trust between human operators and autonomous technologies.

Maritime professionals need tailored explanations: Rather than applying generic XAI solutions, the research advocates for domain-specific approaches that account for the unique operational contexts, pressures, and decision-making processes specific to seafarers and maritime teams.

Human-machine teaming depends on shared understanding: The paper stresses that effective oversight of AI systems in maritime operations requires informed human operators who genuinely understand AI reasoning, not just passive acceptance of automated decisions.

User perspectives should guide XAI design: The proposed domain-specific survey methodology prioritizes capturing maritime professionals' actual perceptions of trust, usability, and explainability—suggesting that end-user input, rather than technical considerations alone, should drive how maritime AI systems are developed and deployed.

Explainability is foundational for responsible maritime automation: As autonomous technologies increasingly take on decision-making roles at sea, the paper positions XAI as essential infrastructure for maintaining human agency, accountability, and safety in maritime operations.