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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, 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]
In the citing paper
“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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Machiavelli for robots: Strategic robot failure, deception, and trust
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Shafi, K. (2017) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2017.7974516 · Cited by 4
Lane, S., Esterwood, C. T., Kulić, D., & Robinson, N. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731465 · Cited by 3
Goo, H. G., Schadenberg, B. R., Kolkmeier, J., Truong, K. P., & Evers, V. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719739 · Cited by 3
Tang, L. R., & Bashir, M. N. (2025) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms65439.2025.11154364 · Cited by 3
Metadata
- 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 › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- trust in robots, trust repair, human-robot interaction, virtual worlds
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