Trust and etiquette in high-criticality automated systems
Abstract
Whereas the other articles in this section discuss human-computer etiquette in traditional social interactions involving the use of computers that explicitly strive to elicit a perception of “personhood ” from the human participant, we focus on computers that occupy more traditional roles as complex and largely unpersonified machines involved in high-criticality working relationships with humans—where the consequences of failure can be catastrophic in terms of lives, money, or both. Politeness and social niceties are important in many human-human social interactions, but in critical, highly technical work, there is the common misperception that we can “dispense with protocol ” and get down to business, even with those who are not particularly courteous. In fact, excessive adherence to polite norms can seem stilted and
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The style in which uncertainty estimates are communicated is highly relevant to how these are perceived.
“prior work suggests that the style in which uncertainty estimates are communicated is highly relevant to how these are perceived [98].”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [98]
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 · see citing works
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · see citing works
Self-citationPerformance Consequences of Automation-Induced 'Complacency'
Parasuraman, R., Molloy, R., & Singh, I. L. (1993) · The International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0301_1 · Cited by 875 · cited 2× · see citing works
“This has been found in studies in which users rated their trust in automation after use [6, 9].”
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Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817 · see citing works
Almost human: Anthropomorphism increases trust resilience in cognitive agents.
de Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A. B., McKnight, P. E., Krueger, F., & Parasuraman, R. (2016) · Journal of Experimental Psychology: Applied · doi:10.1037/xap0000092 · Cited by 614 · see citing works
Self-citationFundamentals of physiological computing
Fairclough, S. H. (2009) · Interacting with Computers · doi:10.1016/j.intcom.2008.10.011 · Cited by 608 · see citing works
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 · see citing works
The effects of transparency on trust in and acceptance of a content-based art recommender
Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9051-3 · Cited by 448 · see citing works
Trusting Automation: Designing for Responsivity and Resilience
Chiou, E. K., & Lee, J. D. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211009995 · Cited by 301 · see citing works
Kraus, J., Scholz, D., Stiegemeier, D., & Baumann, M. (2020) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819853686 · Cited by 276 · see citing works
“It evolves and adapts over time along the accumulating knowledge of the user about a system (trust calibration, Lee & See, 2004; Parasuraman & Miller, 2004).”
BackgroundUncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462571 · Cited by 225 · see citing works
Khastgir, S., Birrell, S., Dhadyalla, G., & Jennings, P. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.07.001 · Cited by 134 · see citing works
A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives
Gebru, B., Zeleke, L., Blankson, D., Nabil, M., Nateghi, S., Homaifar, A., & Tunstel, E. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3144956 · Cited by 101 · see citing works
Merritt, S. M., Lee, D., Unnerstall, J. L., & Huber, K. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814561675 · Cited by 83 · see citing works
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 · see citing works
Assessing Demand for Transparency in Intelligent Systems Using Machine Learning
Vorm, E. S. (2018) · 2018 Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2018.8466328 · Cited by 25 · see citing works
Continuous Calibration of Trust in Automated Systems
Merritt, S. M., Huber, K., LaChapell-Unnerstall, J., & Lee, D. (2014) · doi:10.21236/ada606748 · Cited by 20 · see citing works
Designing Trustworthy User Interfaces
Zieglmeier, V., & Lehene, A. M. (2021) · 33rd Australian Conference on Human-Computer Interaction · doi:10.1145/3520495.3520525 · Cited by 19 · see citing works
The Role of Behavioral Anthropomorphism in Human-Automation Trust Calibration
Jensen, T., Khan, M. M. H., & Albayram, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-50334-5_3 · Cited by 12 · see citing works
Designing Brain-Computer Interfaces for Attention-Aware Systems
Peck, E. M., Carlin, E., & Jacob, R. (2015) · Computer · doi:10.1109/mc.2015.315 · Cited by 8 · see citing works
Lu, Y., & Sarter, N. (2019) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181319631345 · Cited by 5 · see citing works
- Authors
- Parasuraman, R., & Miller, C. A.
- Affiliations
- Catholic University of America (US)
- Smart Information Flow Technologies (United States) (US)
- Venue
- Communications of the ACM
- Published
- 2004-04
- Volume
- 47
- Issue
- 4
- Pages
- 51-55
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W1993904780
- Cited by
- 311
- References
- 3
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 16 · 2023: 14 · 2022: 15 · 2021: 21 · 2020: 17 · 2019: 28 · 2018: 21 · 2017: 15 · 2016: 16 · 2015: 16 · 2014: 14 · 2013: 12 · 2012: 12
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- trust in automation, politeness, social norms, human-computer interaction, personhood, failure consequences
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