2023/04/20 by Matouš Jelínek, Kerstin Fischer, Jelínek, Matouš +1
Psychology · Social Sciences · #Cognitive Functions and Memory #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Human-Computer Interaction (cs.HC) #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2304.10360
openalex publication_date 2023/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While trust in human-robot interaction is increasingly recognized as necessary for the implementation of social robots, our understanding of regulating trust in human-robot interaction is yet limited. In the current experiment, we evaluated different approaches to trust calibration in human-robot interaction. The within-subject experimental approach utilized five different strategies for trust calibration: proficiency, situation awareness, transparency, trust violation, and trust repair. We implemented these interventions into a within-subject experiment where participants (N=24) teamed up with a social robot and played a collaborative game. The level of trust was measured after each section using the Multi-Dimensional Measure of Trust (MDMT) scale. As expected, the interventions have a significant effect on i) violating and ii) repairing the level of trust throughout the interaction. Consequently, the robot demonstrating situation awareness was perceived as significantly more benevolent than the baseline.