Who Should I Blame? Effects of Autonomy and Transparency on Attributions in Human-Robot Interaction

Abstract

As autonomous robots collaborate with people on tasks, the questions "who deserves credit?" and "who is to blame?" are no longer simple. Based on insights from an observational study of a delivery robot in a hospital, this paper deals with how robotic autonomy and transparency affect the attribution of credit and blame. In the study, we conducted a 2times2 experiment to test the effects of autonomy and transparency on attributions. We found that when a robot is more autonomous, people attribute more credit and blame to the robot and less toward themselves and other participants. When the robot explains its behavior (e.g. is transparent), people blame other participants (but not the robot) less. Finally, transparency has a greater effect in decreasing the attribution of blame when the robot is more autonomous