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Agent transparency is an important contributor to human performance, situation awareness (SA), and trust in humanagent teaming. However, agent transparency's effects on human performance when the agent is unreliable have yet to be examined. This paper examined how the transparency and reliability of an autonomous robotic squad member (ASM) affected a human observer's task performance, workload, SA, trust in the robot, and perceptions of the robot. In a 2 (ASM transparency) × 2 (ASM reliability) within-subject design experiment, participants monitored a simulated soldier squad that included an ASM as it traversed a simulated training environment, while concurrently monitoring the environment for targets. There was no difference in participants' performance on the target detection task, workload, or SA due to either ASM transparency or reliability. ASM reliability influenced participant trust and perceptions of the robot. Results suggest that reliability may be a stronger influence on the human's perceptions of the robot than transparency. Robot errors had a profound and lasting effect on the participants' perception of the robot's future reliability and resulted in reduced confidence in their assessments of the robot's reliability. These findings could have important implications for the continued use of automated systems when the user is aware of system errors.

We conducted a human-in-the-loop robot simulation experiment. The effects of displaying transparency information, in the interface for an autonomous robot, on operator trust were examined. Participants were assigned to one of three transparency conditions and trust was measured prior to observing the autonomous robotic agent's progress and post observation. Results demonstrated that participants who received more transparency information reported higher trust in the autonomous robotic agent. Overall findings indicate that displaying SAT model-based transparency information on a robotic interface is effective for appropriate trust calibration in an autonomous robotic agent.

We use the Situation awareness-based Agent Transparency model as a framework to design a user interface to support agent transparency. Participants were instructed to supervise an autonomous robotic agent as it traversed simulated urban environments. During this task, participants were exposed to one of three levels of information used to support agent transparency in the interface display. Our findings suggest that providing agent transparency information allows operators to properly calibrate trust without excess workload. Though, increased agent transparency information did not support operator situation awareness.