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Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations

arXiv 2609.11805 ↗First posted 10 Sep 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

Maritime Autonomous Surface Ships (MASS) and AI- supported decision assistants are expected to transform maritime operations, but their safe integration depends on how maritime professionals perceive and trust such systems. This paper presents a survey study on maritime stakeholders' attitudes toward an AI-supported assistant in collision-avoidance scenarios. Participants evaluated technology anxiety, trust in automation, and explanation quality using established and adapted questionnaires, complemented by sentiment and thematic analysis of open-ended responses Results indicate a generally positive disposition toward maritime technology, no clear age-related differences in openness, stable trust across scenarios, and more scenario-sensitive, multidimensional explanation ratings. Open responses showed that participants valued support for decision-making, situation awareness, and confidence-building, while raising concerns about AI reliability, over- reliance and loss of expertise. The findings suggest that maritime AI systems should not focus solely on increasing automation or trust, but on supporting calibrated reliance through transparent, reliable, and operationally meaningful design with domain experts in the loop.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 32 references that are in the map. First posted on arXiv 10 Sep 2026.

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References · 5

  1. 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
  2. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  3. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 125
  4. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  5. 2024Explainable AI for Ship Collision Avoidance: Decoding Decision-Making Processes and Behavioral Intentionscited by 1

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