Trust in AI is crucial for a cooperative relationship between humans and AI in systems utilizing this technology, such as autonomous driving systems. Trust facilitates the appropriate utilization of these systems, thereby optimizing their potential benefits. If humans over-trust or under-trust an AI, serious problems such as misuse and accidents occur. To prevent over/under-trust, it is necessary to estimate trust. However, trust is an internal state of humans and is hard to observe directly. Therefore, we propose a estimation model for trust using dynamic structure equation modeling, which extends SEM and can handle time-series data. A path diagram, which shows causalities between variables, is developed in an exploratory way and the resultant path diagram is optimized for effective path structures. Over/under-trust was estimated with 99 % accuracy in an autonomous driving task. These results show that our proposed method outperformed the conventional method including an auto regression family.

Trust, the cornerstone of human-robot interaction, is a key element in fostering a synergistic human-robot relationship. Trust facilitates the appropriate utilization of these systems, thereby optimizing their potential benefits. A failure to appropriately gauge the level of trust in a robot can have grave consequences, including potential misuse and accidents, underscoring the critical importance of accurate trust assessment in fostering a harmonious and safe human-robot collaboration. To avert such issues, it is imperative to calibrate trust levels accurately. To address this need, we have developed a novel estimation model for trust, leveraging the capabilities of structural equation modeling (SEM) to address the challenges posed by latent variables. The proposed model demonstrated a 70% accuracy in estimating trust during a manipulator’s successful and failed behaviors with uncertainty. The outcomes demonstrate the efficacy of the proposed method in surpassing conventional approaches.