Agent Transparency and Reliability in Human-Robot Interaction: The Influence on User Confidence and Perceived Reliability

• Published in: IEEE transactions on human-machine systems Vol. 50; no. 3; pp. 254 - 263
• Main Authors: Wright, Julia L., Chen, Jessie Y. C., Lakhmani, Shan G.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agent transparency; Cognition; confidence; Decision making; Environmental monitoring; Human performance; human–robot interaction; Monitoring; Object detection; Perceptions; Reliability; Reliability analysis; Robots; situation awareness (SA); Situational awareness; Target detection; Task analysis; trust; Workload; Workloads
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2019.2925717

Agent transparency is an important contributor to human performance, situation awareness (SA), and trust in human-agent 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.