Maritime Policy & Management

Artificial Intelligence for Maritime Safety Management: Applications of Large Language Models, Intelligent Agents, and Data-Driven Decision Support in Shipping

We invite original research papers, review articles, conceptual papers, and policy-oriented studies addressing AI-enabled maritime safety management. Topics may include, but are not limited to:

LLMs and Generative AI in Maritime Safety

• Large language models for maritime accident analysis and causal inference
•  LLM-assisted extraction of safety knowledge from accident reports, inspection reports, and incident narratives
•  Generative AI for maritime safety training, simulation, and scenario generation
• Retrieval-augmented generation (RAG) for maritime regulation, compliance, and safety decision support
• Domain adaptation and fine-tuning of LLMs for maritime safety language tasks
• Hallucination, reliability, and validation issues in maritime safety applications of LLMs

Intelligent Agents and Agentic AI for Shipping Safety

• AI agents for real-time safety monitoring and operational support
• Multi-agent systems for vessel traffic coordination, collision avoidance, and emergency response
• Autonomous decision-support agents for bridge operations and port management
• Human–AI teaming in ship navigation, fleet monitoring, and maritime control centers
• Agent-based safety governance frameworks in shipping and coastal operations

Machine Learning and Explainable AI for Risk Assessment

• Interpretable machine learning for accident severity, collision, grounding, and fire risk assessment
• Spatial–temporal risk mapping for shipping safety
• Hybrid GIS–AI frameworks for maritime accident hotspot identification
• Near-miss analysis and early warning systems using AIS, meteorological, and operational data
• Bayesian networks, graph learning, and causal AI for safety analysis
• Explainability, fairness, and trust in maritime risk models

Human Factors, Organizational Safety, and Decision Support

• AI for fatigue detection, crew behavior analysis, and human error prevention
• NLP and conversational systems for safety culture assessment
• Decision support for voyage planning, pilotage, and emergency management
• Human-centered design of AI-enabled maritime safety systems
• Training, competence, and trust calibration in AI-supported safety environments

Policy, Governance, and Regulatory Implications

• Governance of AI deployment in maritime safety management
• Legal liability, accountability, and ethical challenges of AI-supported maritime decisions
• IMO-related policy implications of AI-based safety systems
• AI readiness and institutional capacity in maritime authorities and shipping companies
• Standards, auditing, and assurance frameworks for trustworthy maritime AI
• Cybersecurity and resilience challenges in AI-enabled maritime operations

Data, Infrastructure, and Implementation Challenges

• Maritime safety data integration across ships, ports, and regulators
• Multimodal AI using text, image, sensor, AIS, and weather data
• Benchmark datasets for maritime safety AI research
• Transferability and generalizability of AI models across regions and vessel types
• Practical barriers to AI adoption in maritime safety management