How do Users Experience Traceability of AI Systems? Examining Subjective Information Processing Awareness in Automated Insulin Delivery (AID) Systems

When interacting with artificial intelligence (AI) in the medical domain, users frequently face automated information processing, which can remain opaque to them. For example, diabetes may interact daily insulin delivery (AID). However, effective AID therapy requires traceability of decisions for diverse users. Grounded research on human-automation interaction, we study Subjective Information Processing Awareness (SIPA) as a key construct users’ experience explainable AI. The objective present was examine how differing levels an AI algorithm. We developed basic simulation create realistic scenarios experiment N = 80, where examined effect three disclosure SIPA and performance. Attributes serving basis needs calculation were shown users, who predicted system’s after over 60 observations. Results showed difference repeated observations, associated general decline ratings time. Supporting scale validity, strongly correlated trust satisfaction explanations. indicates that different need several repetitions before it manifests. Additionally, high lead miscalibration between performance predicting results. results indicate responsible design XAI, system designers could utilize prediction tasks order calibrate experienced traceability.