Explanations and Expectations: Trust Building in Automated Vehicles
Open accessAbstract
Trust is a vital determinant of acceptance of automated vehicles (AVs) and expectations and explanations are often at the heart of any trusting relationship. Once expectations have been violated, explanations are needed to mitigate the damage. This study introduces the importance of timing of explanations in promoting trust in AVs. We present the preliminary results of a within-subjects experimental study involving eight participants exposed to four AV driving conditions (i.e. 32 data points). Preliminary results show a pattern that suggests that explanations provided before the AV takes actions promote more trust than explanations provided afterward.
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Haspiel et al. investigated the influence of explanations on the trust-building process that were provided at different time frames.
“Contrary to popular belief, Haspiel et al. [57] presented another point of view by investigating the influence of explanations on the trust-building process that were provided at different time frames.”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [57]
Explanations generated before vehicle-taking actions yielded the best trust among humans.
“The study successfully proved that explanations generated before vehicle-taking actions yielded the best trust among humans.”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [57]
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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
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“Contrary to popular belief, Haspiel et al. [57] presented another point of view by investigating the influence of explanations on the trust-building process that were provided at different time frames.”
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A Survey of Data-driven and Knowledge-aware eXplainable AI
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- Authors
- Haspiel, J., Du, N., Meyerson, J., Robert Jr., L. P., Tilbury, D., Yang, X. J., & Pradhan, A. K.
- Affiliations
- University of Michigan (US)
- University of Michigan, Ann Arbor, MI, USA
- Venue
- Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2018-03
- Pages
- 119-120
- OpenAlex
- W2782821189
- Cited by
- 79
- References
- 4
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 16 · 2023: 8 · 2022: 18 · 2021: 12 · 2020: 9 · 2019: 5 · 2018: 4
- FWCI
- 13.43
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- University of Michigan Mcity
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- trust in automated vehicles, explanation, expectations, autonomous vehicles, human-automation interaction, trust building, acceptance of automation
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