The Effects of Inaccurate Decision-Support Systems on Structured Shared Decision-Making for Human-Robot Teams

Human-robot teams can leverage a human's expertise and a robot's computational power to meaningfully improve mission outcomes. In command and control domains, the robot teammate can also act as a decision-support system to advise human users. However, decision-support systems are susceptible to human factors issues including miscalibrated trust and degraded team performance. Recent work has mitigated these issues by using cognitive forcing functions to structure shared decision-making systems and place users as proactive on-the-loop actors. We bring this approach to a human-robot teaming domain, and investigate how Type I and Type II errors in the robot's recommendation affects team performance and user rational trust. We present the architecture of our decision-making process and a Mars rover landing experiment domain. Results from a comprehensive user study demonstrate that the error type of the robot's recommendation forms a trade-off between team performance and rational trust.