Collaborative Decision Making with Responsible AI: Establishing Trust and Load Models for Probabilistic Transparency

Abstract

In responsible AI development, the construction of AI systems with well-designed transparency and the capability to achieve transparency-adaptive adjustments necessitates a clear and quantified understanding of user states during the interaction process. Among these, trust and load are two important states of the user’s internal psychology, albeit often challenging to directly ascertain. Thus, this study employs transparency experiments involving multiple probabilistic indicators to capture users’ compliance and reaction times during the interactive collaboration process of receiving real-time feedback. Subsequently, estimations of trust and load states are established, leading to the further development of a state transition matrix. Through the establishment of a trust–workload model, probabilistic estimations of user states under varying levels of transparency are obtained, quantitatively delineating the evolution of states and transparency within interaction sequences. This research lays the groundwork for subsequent endeavors in optimal strategy formulation and the development of transparency dynamically adaptive adjustment strategies based on the trust–workload state model constraints.