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2020-12-01
Ullman, D., Malle, B. F..  2019.  Measuring Gains and Losses in Human-Robot Trust: Evidence for Differentiable Components of Trust. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). :618—619.

Human-robot trust is crucial to successful human-robot interaction. We conducted a study with 798 participants distributed across 32 conditions using four dimensions of human-robot trust (reliable, capable, ethical, sincere) identified by the Multi-Dimensional-Measure of Trust (MDMT). We tested whether these dimensions can differentially capture gains and losses in human-robot trust across robot roles and contexts. Using a 4 scenario × 4 trust dimension × 2 change direction between-subjects design, we found the behavior change manipulation effective for each of the four subscales. However, the pattern of results best supported a two-dimensional conception of trust, with reliable-capable and ethical-sincere as the major constituents.