The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
Evaluating Effects of User Experience and System Transparency on Trust in Automation
Open accessAbstract
Existing research assessing human operators' trust in automation and robots has primarily examined trust as a steady-state variable, with little emphasis on the evolution of trust over time. With the goal of addressing this research gap, we present a study exploring the dynamic nature of trust. We defined trust of entirety as a measure that accounts for trust across a human's entire interactive experience with automation, and first identified alternatives to quantify it using real-time measurements of trust. Second, we provided a novel model that attempts to explain how trust of entirety evolves as a user interacts repeatedly with automation. Lastly, we investigated the effects of automation transparency on momentary changes of trust. Our results indicated that trust of entirety is better quantified by the average measure of "area under the trust curve" than the traditional post-experiment trust measure. In addition, we found that trust of entirety evolves and eventually stabilizes as an operator repeatedly interacts with a technology. Finally, we observed that a higher level of automation transparency may mitigate the "cry wolf" effect -- wherein human operators begin to reject an automated system due to repeated false alarms.
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In the citing paper
“In addition, some studies also employed linear trust meters [137], or visual analog scales [139].”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [139]
In the citing paper
“Some authors work with correlations to get a better sense of perceived trustworthiness and system capabilities [5,12,24,39,66,67,81,94,130,139,142].Here the larger the correlation between perceived trustworthiness and system capabilities, the better the calibration.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [139]
In the citing paper
“With few exceptions, we have little understanding of how trust strengthens or decays due to moment-to-moment interactions with automation”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Yang et al., 2017
In the citing paper
“trust is a dynamic variable that can change over time”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Yang et al., 2017
In the citing paper
“Critically, the opaqueness of the AI's internal computational processes may decrease calibrated reliance on the system [63 , 64] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [64]
In the citing paper
“Such measures include, but are not limited to measuring end-users' decision time [19,62], how many times end-users requested AI recommendation [52], and how many times they agreed with AI recommendations [7,23,58].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [58]
In the citing paper
“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 [13]
In the citing paper
“Several studies [13, 23, 26] have demonstrated that reliance behavior can be explained by the relationship between a user’s trust in the agents and the user’s self-confidence.”
From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [13]
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Willingness to work as a predictor of human-agent team success
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Tool or Partner: Mental-Aware Human-Robot Collaboration
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Metadata
- Authors
- Yang, X. J., Unhelkar, V. V., Li, K., & Shah, J. A.
- Affiliations
- University of Michigan (US)
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2017-03-06
- Pages
- 408-416
- OpenAlex
- W2594678573
- Cited by
- 208
- References
- 36
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 41 · 2023: 29 · 2022: 22 · 2021: 25 · 2020: 23 · 2019: 18 · 2018: 10 · 2017: 1
- FWCI
- 23.05
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- Singapore University of Technology and Design
- 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, automation transparency, user experience, trust dynamics, false alarms, human-automation interaction, trust measurement
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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