Managing Change in Maritime AI: A Semantic and Modular Framework for Adaptive Maritime Systems

Artificial intelligence (AI) is transforming maritime operations with increasingly autonomous functions. These systems must adapt continuously to changing environmental conditions, new technologies, diverse fleets, and evolving regulations to maintain reliability, safety, and compliance which are critical factors in the maritime industry. This paper presents a semantic and modular framework designed to manage such change through interconnected layers. The base layer employs a maritime knowledge graph based on ISO 19848 Annex C to integrate operational data from sensors and navigation systems. Above this, a layer of modular contracts wraps AI models and system modules, allowing easy updates and verification as conditions evolve. Modular contracts represent formal agreements specifying requirements, acceptance criteria, and interfaces for modules, such as an autonomous navigation system. These contracts define module interactions, enabling reuse across ships and simplifying updates and verification. A federated learning layer supports secure AI training and updates distributed across multiple vessels. An explainable AI layer provides operator insights to foster transparency and trust. The top layer applies dynamic risk management and compliance checks aligned with regulations. An illustrative example of ship propulsion fault detection shows how these layers interoperate-from data integration and modular design to evolving AI, human oversight, and risk management, offering a flexible approach to managing change in maritime AI systems. This framework provides an adaptable and robust foundation for AI solutions that must remain effective and safe in the ever-changing maritime environment.

Keywords: Maritime AI, knowledge graph, modular contracts, risk, change management, assurance, reliability.