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

• URL: http://arxiv.org/abs/2509.15084v1
• Date: Thu, 18 Sep 2025 15:42:54 GMT
• Title: From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support
• Abstract summary: This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain.<n>We propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability.<n>Our aim is to foster awareness and guide the development of user-centric XAI systems tailored to the needs of seafarers and maritime teams.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations [0.0]
  This paper presents a survey study on maritime stakeholders' attitudes toward an AI-supported assistant in collision-avoidance scenarios.<n>Results indicate a generally positive disposition toward maritime technology, no clear age-related differences in openness, stable trust across scenarios, and more scenario-sensitive explanation ratings.<n>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.
  arXiv  Detail & Related papers  (2026-09-10T16:57:44Z)
• A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust [2.4578723416255754]
  Human-Centered AI (HCAI) emphasizes alignment with human values, while Explainable AI (XAI) enhances transparency by making AI decisions more understandable.<n>This paper presents a novel three-layered framework that bridges HCAI and XAI to establish a structured explainability paradigm.<n>Our findings advance Human-Centered Explainable AI (HCXAI), fostering AI systems that are transparent, adaptable, and ethically aligned.
  arXiv  Detail & Related papers  (2025-04-14T01:29:30Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Trustworthy XAI and Application [0.0]
  Explainable Artificial Intelligence (XAI) is important for building trust.<n>XAI helps ensure that AI systems work reliably and ethically.<n>This article looks at XAI and its three main parts: transparency, explainability, and trustworthiness.
  arXiv  Detail & Related papers  (2024-10-22T16:10:10Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations [53.01494092422942]
  The number and complexity of personalised and user-centred approaches to XAI have rapidly grown in recent years. It emerged that user-centred approaches to XAI positively affect the interaction between users and systems. We propose an assessment task to objectively and quantitatively measure the goodness of XAI systems.
  arXiv  Detail & Related papers  (2023-12-07T15:49:39Z)
• Towards Reconciling Usability and Usefulness of Explainable AI Methodologies [2.715884199292287]
  Black-box AI systems can lead to liability and accountability issues when they produce an incorrect decision. Explainable AI (XAI) seeks to bridge the knowledge gap, between developers and end-users.
  arXiv  Detail & Related papers  (2023-01-13T01:08:49Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Knowledge-intensive Language Understanding for Explainable AI [9.541228711585886]
  How AI-led decisions are made and what determining factors were included are crucial to understand. It is critical to have human-centered explanations that are directly related to decision-making. It is necessary to involve explicit domain knowledge that humans understand and use.
  arXiv  Detail & Related papers  (2021-08-02T21:12:30Z)
• Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems [0.8701566919381223]
  We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation. We created a user-interface for our XAI framework and evaluated its efficacy via a human-user experiment.
  arXiv  Detail & Related papers  (2021-06-07T16:38:43Z)
• The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions [4.169915659794568]
  People need more awareness of how AI works and its outcomes to build a relationship with that system. In decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system.
  arXiv  Detail & Related papers  (2021-02-10T14:28:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.