From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support
It addresses the problem of AI transparency for maritime professionals, but it is incremental as it focuses on survey design rather than new methods or systems.
This paper tackles the need for Explainable AI (XAI) in maritime operations to enhance trust and human-machine teaming, proposing a domain-specific survey to capture professionals' perceptions and guide user-centric system development.
As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To support the user-centered integration of XAI, we propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability. Our aim is to foster awareness and guide the development of user-centric XAI systems tailored to the needs of seafarers and maritime teams.