A state-of-the-art review of AI decision transparency for autonomous shipping

Abstract

ABSTRACTThe introduction of maritime autonomous surface ships (MASSs) has prompted a significant shift in the role of navigators from traditional navigation to supervising artificial intelligence (AI) collision avoidance systems or managing operations from remote operation centers (ROCs). This may be problematic because the integration of AI technologies into collision avoidance systems may jeopardize safety if done in a way that reduces human control or leaves humans out of the loop. For onboard navigators or ROC operators who work with AI, it is important that the AI’s “thinking” and decisions are transparent and that alternative decisions are easy to execute. Regarding navigation and collision avoidance, this issue can be defined as AI decision transparency. In this systematic review, we examined state-of-the-art research on traffic alerts and collision avoidance systems with respect to decision transparency for autonomous shipping. Through a thematic analysis, we identified three main groups of transparency in the reviewed literature: strategies, visualization, and technology, with respective subgroups.

Keywords

• MASS
• TCAS
• decision transparency
• ROC
• XAI