Explain yourself! Effects of Explanations in Human-Robot Interaction
This addresses the problem of understanding how explanations impact human-robot interaction for users, though it is incremental as it builds on prior work in explainable AI.
The study investigated whether robots explaining their decisions in a competitive board game affects human perceptions, finding that explanations did not change perceived competence, intelligence, likeability, or safety but made the robot seem more lively and human-like.
Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.