Estimating the Trust of Humans in AI for Level 3 Autonomous Driving

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.