  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.
