The DOI matches its Crossref record, with a small difference in title or year; 2 of the 2 references it cites were found in a registry or an index.
Quantifying Compliance and Reliance Trust Behaviors to Influence Trust in Human-Automation Teams
Abstract
Automation is utilized heavily in many domains to increase productivity. With new, more complex automation, like the self-driving car, humans will be required to forego direct task performance in favor of maintaining a supervisory role over automation systems. While the use of these systems generally results in greater performance than humans performing alone, humans are reluctant to adopt these superior systems due to a lack of trust. The United States Department of Defense is investigating trust in automation in order to influence the rate of adoption of automation technology. Studying trust in automation systems requires a mechanism for quantifying and measuring trust. This paper proposes a method for measuring human trust behaviors with regard to human-automation systems through response rates of compliance and reliance. Using behavioral data from a human-subjects experiment involving automated agents, we create a system dynamics model which relates trust to other system level variables. Using this trust model, engineers will be able to study trust in human-automation team scenarios in order to design automation systems with higher rates of adoption.
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“Miscalibrated trust-whether over- or under-trust-can lead developers to overlook genAIinduced errors and risks [108] or to avoid these tools altogether in work [14].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [14]
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Citations
Cites
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 2×
“Studies on reliance have shown it to be the product of emotion, which was deemed “critical for the appropriate direction of attention since it provides an automated signal about the organism’s past experience” (Lee & See, 2004).”
BackgroundAutomated Vehicle Crash Rate Comparison Using Naturalistic Data
Blanco, M., Atwood, J., Russell, S., Trimble, T. E., McClafferty, J., & Perez, M. (2016) · Virginia Tech Transportation Institute · doi:10.13140/rg.2.1.2336.1048 · Cited by 90
Research works printed but not linked: 9
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- Endsley M. R., 2015, Autonomous Horizons: System Autonomy in the Air Force - A Path to the Future, 1
- Google, 2015, Google Self-Driving Car Testing Report on Disengagements of Autonomous Mode
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Cited by
Investigating and Designing for Trust in AI-powered Code Generation Tools
Wang, R., Cheng, R., Ford, D., & Zimmermann, T. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658984 · Cited by 87
“The lack of trust can prevent users from adopting AI tools in their workflow, even when the system’s performance is superior [8, 47].”
BackgroundBetter Together? An Evaluation of AI-Supported Code Translation
Weisz, J. D., Muller, M., Ross, S. I., Martinez, F., Houde, S., Agarwal, M., Talamadupula, K., & Richards, J. T. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511157 · Cited by 69
The relationship of trust and dependence
Patton, C. E., & Wickens, C. D. (2024) · Ergonomics · doi:10.1080/00140139.2024.2342436 · Cited by 44
Trust with increasing and decreasing reliability
Rittenberg, B. S. P., Holland, C. W., Barnhart, G. E., Gaudreau, S. M., & Neyedli, H. F. (2024) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208241228636 · Cited by 19
A Computational Model of Coupled Human Trust and Self-confidence Dynamics
Williams, K. J., Yuh, M. S., & Jain, N. (2023) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3594715 · Cited by 18
Metadata
- Authors
- Boubin, J. G., Rusnock, C. F., & Bindewald, J. M.
- Affiliations
- Air Force Institute of Technology Wright-Patterson AFB, OH 45433
- Venue
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Publisher
- SAGE Publications
- Published
- 2017-09
- Volume
- 61
- Issue
- 1
- Pages
- 750-754
- ISSN
- 1071-1813
- 2169-5067
- Cited by
- 17
- References
- 2
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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- Boubin, J. G., Rusnock, C. F., & Bindewald, J. M. (2017). Quantifying Compliance and Reliance Trust Behaviors to Influence Trust in Human-Automation Teams. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/1541931213601672
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
