Effects of a Social Robot’s Self-Explanations on How Humans Understand and Evaluate Its Behavior

Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a robot’s ability to self-explain its behavior affects the users’ perception and assessment of this behavior. We propose an explanation model based on humans’ folk-psychological concepts and test different explanation strategies in specifically designed HRI scenarios with robot behaviors perceived as intentional, but differently surprising or desirable. All types of explanation strategies increased the understandability and desirability of the behaviors. While merely stating an action had similar effects as giving a reason for it (an intention or need), combining both in a causal explanation helped the robot to better justify its behavior and to increase its understandability and desirability to a larger extent. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; User studies; Scenario-based design; Empirical studies in interaction design. ACM Reference Format: Sonja Stange and Stefan Kopp. 2020. Effects of a Social Robot’s Self-Explanations on How Humans Understand and Evaluate Its Behavior. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374802