Trust Development in Military and Civilian Human–Agent Teams: The Effect of Social-Cognitive Recovery Strategies
Open accessAbstract
Autonomous agents (AA) will increasingly be deployed as teammates instead of tools. In many operational situations, flawless performance from AA cannot be guaranteed. This may lead to a breach in the human’s trust, which can compromise collaboration. This highlights the importance of thinking about how to deal with error and trust violations when designing AA. The aim of this study was to explore the influence of uncertainty communication and apology on the development of trust in a Human–Agent Team (HAT) when there is a trust violation. Two experimental studies following the same method were performed with (I) a civilian group and (II) a military group of participants. The online task environment resembled a house search in which the participant was accompanied and advised by an AA as their artificial team member. Halfway during the task, an incorrect advice evoked a trust violation. Uncertainty communication was manipulated within-subjects, apology between-subjects. Our results showed that (a) communicating uncertainty led to higher levels of trust in both studies, (b) an incorrect advice by the agent led to a less severe decline in trust when that advice included a measure of uncertainty, and (c) after a trust violation, trust recovered significantly more when the agent offered an apology. The two latter effects were only found in the civilian study. We conclude that tailored agent communication is a key factor in minimizing trust reduction in face of agent failure to maintain effective long-term relationships in HATs. The difference in findings between participant groups emphasizes the importance of considering the (organizational) culture when designing artificial team members.
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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 [66]
The Introduction > Trust section describes calibration as a balanced relation between perceived and actual trustworthiness. It does not say that correlations between perceived trustworthiness and system capabilities measure calibration, or that a larger correlation means better calibration. The correlations reported in Results > Study II concern personality traits and initial trust instead; page numbers are unavailable in our copy. (LLM, conf=0.83)
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- Authors
- Kox, E. S., Siegling, L. B., & Kerstholt, J. H.
- Affiliations
- Netherlands Organisation for Applied Scientific Research (NL)
- Utrecht University (NL)
- Corresponding author
- Kox, E. S.
- Venue
- International Journal of Social Robotics
- Published
- 2022-04-10
- Volume
- 14
- Issue
- 5
- Pages
- 1323-1338
- ISSN
- 1875-4791 (ISSN-L)
- 1875-4805
- PMC
- PMC8994847
- OpenAlex
- W4223647913
- Cited by
- 22
- References
- 68
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 2 · 2023: 4 · 2022: 1 · 2020: 1
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Social Robotics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Twente Research Information (acceptedVersion, cc-by)
- TNO Repository (submittedVersion, cc-by)
- University of Twente Research Information (submittedVersion, cc-by)
- Funders
- Ministerie van Defensie
- 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 › Multi-Agent Systems and Negotiation
- Keywords
- human-agent teams, trust building, breach of trust, trust repair, uncertainty communication, apology, autonomous agents, military teams, organizational culture, agent communication, human-robot collaboration
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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