Lie, Repent, Repeat: Exploring Apologies after Repeated Robot Deception

Abstract

This work presents an empirical study of repeated robot deception and its effects on changes in behavior and trust in a human-robot interaction scenario. 715 online and 50 in-person participants completed a multitrial driving simulation in which the car's robot assistant repeatedly lies and apologizes. Through a mixed-method approach, our results show that apologies that offer justifications for deception in our scenario mitigate the negative effects on trust over multiple trials. However, given the time-sensitive, high-risk nature of our scenario, none of the apologies caused people to significantly change their decision to exceed the speed limit while rushing their dying friend to the hospital. These results add much needed knowledge to the understudied area of robot deception and could inform designers and policymakers of future practices when considering deploying robots that may learn to deceive.