Evaluating Effects of User Experience and System Transparency on Trust in Automation
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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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Some studies employed linear trust meters or visual analog scales.
“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]
Some authors use correlations between perceived trustworthiness and system capabilities to measure calibration; larger correlation indicates better calibration.
“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]
Opaqueness of AI's internal computational processes may decrease calibrated reliance on the system.
“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]
Such behavioral measures include decision time, number of recommendation requests, and number of agreements with AI recommendations.
“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]
Most existing studies on trust calibration have investigated how to maintain appropriate trust calibration by continuously presenting system information to prevent over-trust or under-trust.
“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]
Reliance behavior can be explained by the relationship between a user's trust in the agents and the user's self-confidence.
“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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- 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 › Psychology › Social Psychology › 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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