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Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems
Abstract
Today many systems are highly automated. The human operator's role in these systems is to supervise the automation and intervene to take manual control when necessary. The operator's choice of automatic or manual control has important consequences for system performance, and therefore it is important to understand and optimize this decision process. One important determinant of operators' choice of manual or automatic control may be their degree of trust in the automation. However, there have been no experimental tests of this hypothesis until recently, nor is there a model of human trust in machines to form a theoretical foundation for empirical studies. In this paper a model of human trust in machines is developed, taking models of trust between people as a starting point, and extending them to the human-machine relationship. The resulting model defines human trust in machines and specifies how trust changes with experience on a system, providing a framework for experimental research on trust and human intervention in automated systems.
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“Most of the studies investigated how to maintain appropriate trust calibration by continuously presenting system information to prevent over-trust or under-trust.”
From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [2]
In the citing paper
“Successful collaborations between users and agents would require the users to appropriately adjust their level of trust with the actual reliability of the agents.”
From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [2]
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BackgroundKao, C.-W., & Reynolds, B. L. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03522-3 · Cited by 26
“The superiority of the perceived automated feedback over the perceived teacher feedback might be explained by the human-automation trust theory (Muir, 1994; Muir and Moray, 1996).”
Vertrauensunterstützung für sozio-technische ubiquitäre Systeme
Söllner, M., Hoffmann, A., Hoffmann, H., & Leimeister, J. M. (2012) · Journal of Business Economics · doi:10.1007/s11573-012-0584-x · Cited by 19
“Dabei gilt es zu beachten, dass ein sozio-technisches ubiquitäres System (als Vertrauensnehmer) vom Nutzer als Werkzeug angesehen wird, von dessen Nutzung er sich Unterstützung beim Erreichen eines bestimmten Ziels erwartet (Muir 1994; Lee und See 2004).”
BackgroundInfluentialExploring the impact of trust information visualization on mobile application usage
Yan, Z., Liu, C., Niemi, V., & Yu, G. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-013-0636-4 · Cited by 20
“Muir found a positive correlation between trust and use [39, 40].”
BackgroundResultLee, J., Yamani, Y., Long, S. K., Unverricht, J., & Itoh, M. (2021) · Ergonomics · doi:10.1080/00140139.2021.1909752 · Cited by 18
“To examine the broader context of the meaning of automation trust, Muir (1987, 1994) outlined a framework based on Barber (1983)’s definition, which included expectations of persistence, technical competence, and fiduciary responsibility.”
BackgroundMethodInfluentialIto‐Jaeger, S., Vallejos, E. P., Curran, T., & Crawford, P. (2022) · Health Expectations · doi:10.1111/hex.13507 · Cited by 18
“Previous studies show that the creator's credibility positively influences users' trust in digital health resources, and trust is one of the key factors for deciding whether to use a particular kind of technology.(45) Thus, future media creators should ensure that the involvement of the established institutions is cle…”
BackgroundThe Influence of Feedback on Automation Use, Misuse, and Disuse
Dzindolet, M. T., Pierce, L. G., Peterson, S., Purcell, L., Beck, H. P., & Beck, H. (2002) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120204600370 · Cited by 18
The effects of information accuracy on user trust and compliance
Fox, J. E. (1996) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/257089.257119 · Cited by 18
Warning Signals for Poor Performance Improve Human-Robot Interaction
Van den Brule, R., Bijlstra, G., Dotsch, R., Haselager, P., & Wigboldus, D. H. J. (2016) · Journal of Human-Robot Interaction · doi:10.5898/jhri.5.2.van_den_brule · Cited by 17
“From a psychological perspective, trust is seen as an important variable that mediates a human’s acceptance of technology (Muir, 1994; Parasuraman & Riley, 1997).”
Eckhardt, S., Kühl, N., Dolata, M., & Schwabe, G. (2025) · ACM Computing Surveys · doi:10.1145/3776528 · Cited by 38
Trust metrics in information fusion
Blasch, E. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2050255 · Cited by 17
“Trust metrics have been discussed in various domains such as communications [1], psychology [2], and human interaction [3, 4]; however, the combination of these “trust” metrics for a system has yet to be defined.”
BackgroundBeyond the Buzzwords: On the Perspective of AI in UX and Vice Versa
Wallach, D., Flohr, L. A., & Kaltenhauser, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50334-5_10 · Cited by 17
“With increasing system experience people calibrate their level of trust [22], i.e. they adjust their trust level to match system capabilities, which eventually leads to appropriate use [21].”
BackgroundAn occupant-centered approach to improve both his comfort and the energy efficiency of the building
Boulmaiz, F., Reignier, P., & Ploix, S. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108970 · Cited by 18
A User Behavior Based Trust Model for Mobile Applications
Yan, Z., Niemi, V., Dong, Y. H., & Yu, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69295-9_36 · Cited by 17
“Muir tested her theory in two studies [4, 13].”
BackgroundDrnec, K., & Metcalfe, J. (2016) · Interacción · doi:10.1007/978-3-319-39952-2_16 · Cited by 21
Calibrating Trust in Human-Drone Cooperative Navigation
Okamura, K., & Yamada, S. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223509 · Cited by 17
“This process is called trust calibration [1], [2].”
BackgroundUnderstanding the Formation of Trust
Söllner, M., Hoffmann, A., Hoffmann, H., Wacker, A., & Leimeister, J. M. (2014) · Springer eBooks · doi:10.1007/978-3-319-05044-7_3 · Cited by 20
How to use behavioral research insights on trust for HCI system design
Söllner, M., Hoffmann, A., Hoffmann, H., & Leimeister, J. M. (2012) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2212776.2223696 · Cited by 17
Investigating Cross-Cultural Differences in Trust Levels of Automotive Automation
Yerdon, V., Marlowe, T. A., Volante, W. G., Li, S., & Hancock, P. A. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-41636-6_15 · Cited by 20
An Empirical Evaluation of the Factors Affecting Trust in Web Bankng Systems
Choobineh, J., & Kini, A. (2000) · Journal of the Association for Information Systems · Cited by 17
In loco intellegentia: human factors for the future European train driver
Young, M. S., Stanton, N. A., & Walker, G. H. (2006) · International Journal of Industrial and Systems Engineering · doi:10.1504/ijise.2006.010388 · Cited by 17
“Muir (1994) developed a model of trust in machines, based on previous models of interpersonal trust.”
Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning
Kraus, M., Wagner, N., Riekenbrauck, R., & Minker, W. (2023) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3565472.3595611 · Cited by 20
“Thus, a DA needs to appropriately balance reactive and different kinds of proactive behavior for enhancing cooperation. to find the fine line on improving the user’s task success, but also not to lose the user’s trust which may lead to the system’s disusage [46].”
BackgroundEffects of Displaying Trust Information on Mobile Application Usage
Yan, Z., Liu, C., Niemi, V., & Yu, G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16576-4_8 · Cited by 16
Müller, T., Colley, M., Dogru, G., & Rukzio, E. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3546712 · Cited by 20
“Trust or distrust directly influences passenger’s attitudes towards and usage of automation [54] and is an influential factor concerning the acceptance of automation by humans.”
Background
Metadata
- Authors
- MUIR, B. M.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- MUIR, BONNIE M.
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 1994-11
- Volume
- 37
- Issue
- 11
- Pages
- 1905-1922
- ISSN
- 0014-0139 (ISSN-L)
- 1366-5847
- OpenAlex
- W2032426551
- Cited by
- 873
- References
- 49
- Citations by year
- 2026: 33 · 2025: 52 · 2024: 46 · 2023: 41 · 2022: 51 · 2021: 72 · 2020: 60 · 2019: 44 · 2018: 50 · 2017: 36 · 2016: 46 · 2015: 17 · 2014: 29 · 2013: 26 · 2012: 27
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ergonomics (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- trust in automation, human-machine trust, human intervention, manual control, automated systems, operator decision-making, trust model, human oversight
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