Timing Is Key for Robot Trust Repair
Open accessAbstract
Even the best robots will eventually make a mistake while performing their tasks. In our past experiments, we have found that even one mistake can cause a large loss in trust by human users. In this paper, we evaluate the effects of a robot apologizing for its mistake, promising to do better in the future, and providing additional reasons to trust it in a simulated office evacuation conducted in a virtual environment. In tests with 319 participants, we find that each of these techniques can be successful at repairing trust if they are used when the robot asks the human to trust it again, but are not successful when used immediately after the mistake. The implications of these results are discussed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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In an emergency setting, an apology immediately after a trust violation did not recover trust, but an apology right before the next trust decision point repaired trust.
“In an emergency setting, where an apology right after violated trust has not recovered trust, an apology right before the next trust decision point has repaired trust.”
From Taxonomy of Trust-Relevant Failures and Mitigation Strategies (2020) · cites this paper as [63]
Promises lead to better trust recovery than apologies, and message timing and exact content are crucial.
“Promises lead to a better trust recovery than apologies, and in general, the message timing and exact content was shown to be crucial [63].”
From Taxonomy of Trust-Relevant Failures and Mitigation Strategies (2020) · cites this paper as [63]
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Developing A Dynamic Testbed: Designing for Trust in Complex Human Agent Teams
Nguyen, D., Carmody, K., Wildman, J. L., & Carroll, M. B. (2022) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms56717.2022.9980750 · Cited by 6 · see citing works
Rethinking Trust Repair in Human-Robot Interaction
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Mitigative Strategies for Recovering From Large Language Model Trust Violations
Martell, M. J., Baweja, J. A., & Dreslin, B. D. (2024) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434241303577 · Cited by 5 · see citing works
Trust-aware control in proximate human-robot teaming
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Machiavelli for robots: Strategic robot failure, deception, and trust
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- Authors
- Robinette, P., Howard, A. M., & Wagner, A. R.
- Affiliations
- Georgia Institute of Technology (US)
- Georgia Tech Research Institute (US)
- Corresponding author
- Robinette, Paul
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 574-583
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2398251251
- Cited by
- 121
- References
- 17
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 14 · 2023: 19 · 2022: 15 · 2021: 16 · 2020: 15 · 2019: 9 · 2018: 7 · 2017: 6 · 2016: 1
- FWCI
- 8.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion, cc-by-nc)
- 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 › Artificial Intelligence › AI in Service Interactions
- Keywords
- trust in robots, trust repair, human-robot interaction, virtual worlds
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