Smart Transparency: A User-Centered Approach to Improving Human-Machine Interaction in High-Risk Supervisory Control Tasks

In supervisory control tasks, particularly in high-risk fields, operators need to collaborate with automated intelligent agents to manage dynamic, time-sensitive, and uncertain information. Effective human-agent collaboration relies on transparent interface communication to align with the operator's cognition and enhance trust. This paper proposes a human-centered adaptive transparency information design framework (ATDF), which dynamically adjusts the display of transparency information based on the operator's needs and the task type. This ensures that information is accurately conveyed at critical moments, thereby enhancing trust, task performance, and interface usability. Additionally, the paper introduces a novel user research method, Heu-Kano, to explore the prioritization of transparency needs and presents a model based on eye-tracking and machine learning to identify different types of human-agent interactions. This research provides new insights into human-centered explainability in supervisory control tasks.