Kantwon Rogers
ABSTRACT
This work presents an empirical study into robot deception and its effects on changes in behavior and trust in a high-stakes, time-sensitive human-robot interaction scenario. Specifically, we explore the effectiveness of different apologies to repair trust in an assisted driving task after participants realize they have been lied to by a robotic assistant. Our results show that participants are significantly more likely to change their speeding behaviors when driving advice is framed as coming from a robotic assistant. Our results also suggest an apology without acknowledging intentional deception is best at mitigating negative influences on trust. These results add much needed knowledge to the understudied area of robot deception and could inform designers and policy makers of future practices when considering deploying robots that may learn to deceive.
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Index Terms
Lying About Lying: Examining Trust Repair Strategies After Robot Deception in a High-Stakes HRI Scenario
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