  Recent advances in artificial intelligence (AI) and robotics have drawn
attention to the need for AI systems and robots to be understandable to human
users. The explainable AI (XAI) and explainable robots literature aims to
enhance human understanding and human-robot team performance by providing users
with necessary information about AI and robot behavior. Simultaneously, the
human factors literature has long addressed important considerations that
contribute to human performance, including human trust in autonomous systems.
In this paper, drawing from the human factors literature, we discuss three
important trust-related considerations for the design of explainable robot
systems: the bases of trust, trust calibration, and trust specificity. We
further detail existing and potential metrics for assessing trust in robotic
systems based on explanations provided by explainable robots.
