The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
"The robot may not notice my discomfort" – Examining the Experience of Vulnerability for Trust in Human-Robot Interaction
Abstract
Ensuring trust in human-robot interaction (HRI) is considered essential for widespread use of robots in society and everyday life. While the majority of studies use game-based and high-risk scenarios with low familiarity to gain a deeper understanding of human trust in robots, scenarios with more subtle trust violations that could happen in everyday life situations are less often considered. In this paper, we present a theory-driven approach to studying the situated trust in HRI by focusing on the experience of vulnerability. Focusing on vulnerability not only challenges previous work on trust in HRI from a theoretical perspective, but is also useful for guiding empirical investigations. As a first proof-of-concept study, we conducted an interactive online survey that demonstrates that it is possible to measure human experience of vulnerability in the ordinary, mundane, and familiar situation of clothes shopping. We conclude that the inclusion of subtle trust violation scenarios occurring in the everyday life situation of clothes shopping enables a better understanding of situated trust in HRI, which is of special importance when considering more near-future applications of robots.
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Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 1×
“Trust has always been a topic of interest to research in robotics and was first motivated by studies on trust in automation and the development of collaborative robots [1], [2].”
BackgroundA Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811417254 · Cited by 2,051 · cited 1×
“Today, an increasing body of literature in human-robot interaction (HRI) has been devoted to studying trust, with the main objective of determining which factors influence human perception of robots as trustworthy [4].”
BackgroundInteractions with robots: the truths we reveal about ourselves
Broadbent, E. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-010416-043958 · Cited by 547
Overtrust of robots in emergency evacuation scenarios
Robinette, P., Li, W., Allen, R., Howard, A. M., & Wagner, A. R. (2016) · 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2016.7451740 · Cited by 468
Vulnerable robots positively shape human conversational dynamics in a human–robot team
Traeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B., & Christakis, N. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910402117 · Cited by 140
Taxonomy of Trust-Relevant Failures and Mitigation Strategies
Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T. M., Dixon, C., & Tielman, M. L. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374793 · Cited by 103
Self-citationAroyo, A. M., Rea, F., Sandini, G., & Sciutti, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2856272 · Cited by 85
When Should a Robot Apologize? Understanding How Timing Affects Human-Robot Trust Repair
Nayyar, M., & Wagner, A. R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-05204-1_26 · Cited by 28
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Research works printed but not linked: 37
- zörner, 2021, An Immersive Investment Game to Study Human-Robot Trust, Frontiers in Robotics and AI, 8, 1, 10.3389/frobt.2021.644529 · DOI
- 10.1016/S0963-8687(02)00020-3 · DOI · Paper page
- paridon, 2006, The income effect in Personal Shopping Values, Consumer Self-confidence, and Information Sharing (Word of Mounth Communication) Research, Academy of Marketing Studies Journal, 10, 107
- 10.1086/209376 · DOI · Paper page
- 10.1075/is.7.3.14nom · DOI
- 10.1109/ROMAN.2016.7745167 · DOI
- miles, 2014, Qualitative Data Analysis A Sourcebook of New Methods
- 10.1007/s10514-018-9771-0 · DOI
- 10.1080/07370020903586662 · DOI · Paper page
- ullman, 2018, What Does it Mean to Trust a Robot? Steps Toward a Multidimensional Measure of Trust, Proceedings of 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 263
- 10.1109/ROMAN.2016.7745165 · DOI
- 10.1145/2909824.3020211 · DOI
- sebo, 2019, ’I Don’t Believe You’: Investigating the Effects of Robot Trust Violation and Repair, ACM/IEEE International Conference on Human-robot Interaction, 57
- 10.1007/s10676-011-9279-1 · DOI
- 10.1109/HRI.2019.8673090 · DOI
- 10.1177/001872676001300202 · DOI
- 10.1016/j.sheji.2018.03.001 · DOI
- coeckelbergh, 2013, Human being risk Enhancement technology and the evaluation of vulnerability transformations
- lewis, 2018, The Role of Trust in Human-Robot Interaction, Foundations of Trusted Autonomy (Studies in Systems Decision and Control, 177, 135, 10.1007/978-3-319-64816-3_8 · DOI
- 10.1145/3171221.3171264 · DOI · Paper page
- möllering, 2006, Trust Reason Routine Reflexivity
- 10.1177/1071181311551298 · DOI
- 10.1007/s12369-012-0144-0 · DOI
- 10.1007/978-1-4899-7668-0_10 · DOI
- 10.1109/MIS.2013.24 · DOI
- 10.1007/978-3-319-41959-6_29 · DOI
- 10.1518/hfes.46.1.50.30392 · DOI · Paper page
- 10.5465/AMR.1995.9508080335 · DOI · Paper page
- 10.1109/ROMAN.2008.4600725 · DOI
- misztal, 2011, The Challenges of Vulnerability In Search of Strategies for a Less Vulnerable Social Life
- 10.1007/978-3-030-62056-1_38 · DOI
- martelaro, 2016, Tell me more: Designing HRI to encourage more trust, disclosure, and companionship, ACM/IEEE International Conference on Human-robot Interaction, 181
- 10.1145/2696454.2696497 · DOI · Paper page
- 10.1109/ROMAN.2014.6926359 · DOI · Paper page
- luhmann, 1988, Familiarity, Confidence, Trust: Problems and Alternatives, Trust Making and Breaking Cooperative Relations, 94
- 10.1007/978-3-319-25554-5_58 · DOI
- 10.1145/3171221.3171275 · DOI
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Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200
Metadata
- Authors
- Hannibal, G., Weiss, A., & Charisi, V.
- Affiliations
- TU Wien,Institute of Visual Computing and Human-Centered Technology
- The EU Commission,Joint Research Center (JRC)
- Venue
- 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)
- Publisher
- IEEE
- Published
- 2021-08-08
- Pages
- 704-711
- Cited by
- 11
- References
- 8
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