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Can You Trust Your Trust Measure?

Published:08 March 2021Publication History

ABSTRACT

Trust in human-robot interactions (HRI) is measured in two main ways: through subjective questionnaires and through behavioral tasks. To optimize measurements of trust through questionnaires, the field of HRI faces two challenges: the development of standardized measures that apply to a variety of robots with different capabilities, and the exploration of social and relational dimensions of trust in robots (e.g., benevolence). In this paper we look at how different trust questionnaires (Lyons & Guznov, 2019; Schaefer, 2016; Ullman & Malle, 2018) fare given these challenges that pull in different directions (being general vs. being exploratory) by studying whether people think the items in these questionnaires are applicable to different kinds of robots and interactions. In Study 1 we show that after being presented with a robot (non-humanoid) and an interaction scenario (fire evacuation), participants rated multiple questionnaire items such as "This robot is principled" as "Non-applicable to robots in general" or "Non-applicable to this robot." In Study 2 we show that the frequency of these ratings change (indeed, even for items rated as N/A to robots in general) when a new scenario is presented (game playing with a humanoid robot). Finally, while overall trust scores remained robust to N/A ratings, our results revealed potential fallacies in the way these scores are commonly interpreted. We conclude with recommendations for the development, use and results-reporting of trust questionnaires for future studies, as well as theoretical implications for the field of HRI.

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      • Published in

        cover image ACM Conferences
        HRI '21: Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
        March 2021
        425 pages
        ISBN:9781450382892
        DOI:10.1145/3434073
        • General Chairs:
        • Cindy Bethel,
        • Ana Paiva,
        • Program Chairs:
        • Elizabeth Broadbent,
        • David Feil-Seifer,
        • Daniel Szafir

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        • Published: 8 March 2021

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