Empirical Evaluations of Framework for Adaptive Trust Calibration in Human-AI Cooperation - Kazuo Okamura; Seiji Yamada (IEEE Access, 2020)
Recent advances in AI technologies are dramatically changing the world and impacting our daily life. However, human users still essentially need to cooperate with AI systems to complete tasks as such technologies are never perfect. For optimal performance and safety in human-AI cooperation, human users must appropriately adjust their level of trust to the actual reliability of AI systems. Poorly calibrated trust can be a major cause of serious issues with safety and efficiency. Previous works on trust calibration have emphasized the importance of system transparency for avoiding trust miscalibration. Measuring and influencing trust are still challenging issues; consequently, not many studies have focused on how to detect improper trust calibration nor how to mitigate it. We approach these research challenges with a behavior-based approach to capture the status of calibration. A framework of adaptive trust calibration is proposed, including a formal definition of improper trust calibration called “a trust equation”. It involves cognitive cues called “trust calibration cues (TCCs)” and a conceptual entity called “trust calibration AI” (TCAI), which supervises the status of trust calibration. We conducted empirical evaluations using a simulated drone environment with two types of cooperative tasks: a visual search task and a real-time navigation task. We designed trust changing scenarios and evaluated our framework. The results demonstrated that adaptively presenting a TCC could promote trust calibration more effectively than a traditional system transparency approach.