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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]
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“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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BackgroundInfluentialThe Influence of Cultural Factors on Trust in Automation
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“Trust is taken to be a state, or intervening variable (Muir, 1994).”
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“Therefore, Muir (1994) proposed automation trust calibration, which is the matching relationship between the operator ’ s level of trust in automation and the actual capability of automation.”
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Westin, C., & Lundberg, J. (2025) · Cognition Technology & Work · doi:10.1007/s10111-025-00793-x · Cited by 3
“Age has long been recognized as a factor influencing trust in automation (Muir 1994; Parasuraman and Riley 1997; Mouloua et al. 2002; Lee and See 2004).”
Kaufman, R. P., Lee, E., Bedmutha, M. S., Kirsh, D., & Weibel, N. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713188 · Cited by 9
“It has long been established that trust is a major determinant of adoption of AI-based technologies like AVs, where people’s beliefs about a system directly inform their willingness to accept and rely on the system [64, 84].”
TrustNet: Deep Ensemble Learning for EEG-Based Trust Recognition in Human–Robot Cooperation
Xu, C., Zhang, C., Zhou, Y., Wang, Z., & He, B. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3587542 · Cited by 3
“This approach utilizes Likert scales to assess the level of trust [7].”
Investigating the impact of interaction design characteristics on trust in automated vehicles
Wu, Y., Yao, X., Deng, F., & Yuan, X.-F. (2025) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.103352 · Cited by 1
An, Y., & Niu, J. (2025) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2025.103848 · Cited by 2
Research on the influencing factors of human Drivers' Trust in fully autonomous vehicles
Cai, Y., Chen, Y., Tang, R., & Song, W. (2026) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2026.103509 · Cited by 4
Do emotions matter in AI? The mediating role of emotional response between perceived risk and trust
Babiker, A., Almourad, M. B., Alshakhsi, S. A. A., Liebherr, M., & Ali, R. (2026) · AI & Society · doi:10.1007/s00146-026-02929-7 · Cited by 3
“Trust in AI systems influences the acceptance, usage, and satisfaction of these systems (Muir 1994; Muir and Moray 1996; Stuck et al. 2022).”
Trust in automation: A survey of neurophysiological perspectives on measurement and modelling
Jui, J. J., Hettiarachchi, I. T., & Bhatti, A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133505 · Cited by 3
Trust Management in Mobile Platforms
Yan, Z., Cheng, Y., Yan, P., & Deng, R. H. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812282-2.00005-x · Cited by 2
Systems approach to modeling controversy in Human factors and ergonomics (HFE)
Poornikoo, M., & Mansouri, M. (2023) · International Symposium on Service Oriented Software Engineering · doi:10.1109/sose59841.2023.10178634 · Cited by 2
Vertrauen als Motor des KI-Wertschöpfungszyklus
Glinz, D. (2024) · Springer eBooks · doi:10.1007/978-3-658-43816-6_4 · Cited by 2
Land Administration and Automation in Uganda
Nkote, N. (2012) · InTech eBooks · doi:10.5772/48053 · Cited by 2
Cha, S., Ulbrich, M., Weigl, A., Beckert, B., Land, K., & Vogel‐Heuser, B. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972210 · Cited by 2
“Muir [9] discussed the trust of human (supervisor) in automated system operation as one important determinant of the choice of human among manual and automatic control.”
BackgroundTheoretical Foundations and Literature Review
Meister, S. (2012) · Gabler Verlag eBooks · doi:10.1007/978-3-8349-4055-1_2 · Cited by 2
A Trust Perspective to Study the Intentions of Consumers to the Group Buying
Chen, D., Yang, Y.-S., & Ku, Y.-C. (2012) · Lecture notes in business information processing · doi:10.1007/978-3-642-29873-8_15 · Cited by 2
Tactical Reconnaissance Using Groups of Partly Autonomous UGVs
Svenmarck, P., Andersson, D., Lindahl, B., Hedström, J., & Lif, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02728-4_35 · Cited by 2
“... completing each trial, the participants completed three questionnaires for mental workload using the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) [18], situation awareness using the three dimensional (3-D) Situation Awareness Rating Technique (SART) [19], and trust in the autonomous s…”
MethodThe Role of Trust in the Intention to Use Feedback from Reputation Systems
Vannoy, S. A., Nath, A. K., & Iyer, L. (2008) · Journal of the Association for Information Systems · Cited by 2
Trust Evaluation through Human-Machine Dialogue Modelling
Crocquesel, C., Legras, F., & Coppin, G. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21741-8_53 · Cited by 2
“see the work of Sutcliffe [16]), Muir [ 11 ] leveraged three parameters (issued from Rempel’s work [14]) in order to describe the dynamics of trust:”
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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