Explanations and trust: What happens to trust when a robot partner does something unexpected?

Performance within Human-Autonomy Teams (HATs) is influenced by the effectiveness of communication be-tween humans and robots. Communication is particularly important when robot teammates engage in behaviors that were not anticipated by the human teammate which could degrade trust. However, the literature on trust repair focuses on the role of apologies which may not be appropriate for an unexpected behavior since this behavior may not be an error. Explanations are one method that can be used by robot teammates to avoid costly trust degradation when human expectations are violated. The current study used an Urban Search and Rescue (USAR) scenario to examine the role of explanation in a context wherein a robot teammate deviated from an expected behavior. The current study examined how trust, trustworthiness, and responsibility attribution were influenced by observing the robot teammate deviate from an expected behavior. Participants (N = 148) used an online platform to view videos of the robot: 1) following a planned search path, and 2) deviating from a planned search path. A debriefing event between the human and the robot followed each search activity. Four expla-nation conditions were tested in the debriefing phase following the behavioral violation. Results showed that trust and trustworthiness (ability, benevolence, and integrity) declined following the unexpected behavior. Accordingly, responsibility attribution shifted from the human to the robot following the unexpected behavior. Explanation strategies that focused on communicating why the event occurred by highlighting the robot's environmental awareness were most effective at buffering the decline in trust and trustworthiness.

Automated summary

Communication plays a crucial role in the performance of Human-Autonomy Teams (HATs), especially when robot teammates exhibit unexpected behaviors that may degrade trust. The current study investigated the use of explanations by robot teammates to prevent trust degradation when human expectations are violated. Using an Urban Search and Rescue (USAR) scenario, the study found that explanations focusing on the robot's environmental awareness were most effective at buffering the decline in trust and trustworthiness following unexpected behavior.