The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management
Open accessAbstract
Objective: We investigated the effects of level of agent transparency on operator performance, trust, and workload in a context of human–agent teaming for multirobot management. Background: Participants played the role of a heterogeneous unmanned vehicle (UxV) operator and were instructed to complete various missions by giving orders to UxVs through a computer interface. An intelligent agent (IA) assisted the participant by recommending two plans—a top recommendation and a secondary recommendation—for every mission. Method: A within-subjects design with three levels of agent transparency was employed in the present experiment. There were eight missions in each of three experimental blocks, grouped by level of transparency. During each experimental block, the IA was incorrect three out of eight times due to external information (e.g., commander’s intent and intelligence). Operator performance, trust, workload, and usability data were collected. Results: Results indicate that operator performance, trust, and perceived usability increased as a function of transparency level. Subjective and objective workload data indicate that participants’ workload did not increase as a function of transparency. Furthermore, response time did not increase as a function of transparency. Conclusion: Unlike previous research, which showed that increased transparency resulted in increased performance and trust calibration at the cost of greater workload and longer response time, our results support the benefits of transparency for performance effectiveness without additional costs. Application: The current results will facilitate the implementation of IAs in military settings and will provide useful data to the design of heterogeneous UxV teams.
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In the citing paper
“at least fve studies (see e.g., [4,130]) used the trust predisposition scale by McKnight et al. [91]”
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 [91]
In the citing paper
“However, we also noticed that only a few studies diferentiated between diferent subconstructs of trust (performance, process, purpose) which is inherently included in many scales (e.g., [91]) but authors did not make use of this information.”
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 [91]
In the citing paper
“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 [63]
In the citing paper
“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 [12]
Citations
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“The opera- tor’s SA of the IA’s intent, reasoning, projected outcomes, and uncertainty should improve the operator’s subjective trust as well as trust calibration, which is proper reliance when the IA is correct and correct rejection when the IA is incorrect (Lee & See, 2004).”
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Cited by
Sorted by citations
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816634228 · Cited by 768
Self-citationHuman–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N. J., Barron, A. H., & Schelble, B. G. (2020) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 681
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Metadata
- Authors
- Mercado, J. E., Rupp, M. A., Chen, J. Y. C., Barnes, M. J., Barber, D., & Procci, K.
- Affiliations
- University of Central Florida (US)
- Corresponding author
- Mercado, Joseph E.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2016-02-11
- Volume
- 58
- Issue
- 3
- Pages
- 401-415
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2321972046
- Semantic Scholar
- 5161e7babcca03e87642f3a130c2d25e338bde9a
- Cited by
- 403
- References
- 44
- Citations by year
- 2026: 32 · 2025: 55 · 2024: 46 · 2023: 47 · 2022: 44 · 2021: 41 · 2020: 45 · 2019: 37 · 2018: 25 · 2017: 16 · 2016: 14
- FWCI
- 48.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Control and Systems Engineering › Military Strategy and Technology
- Keywords
- agent transparency, human-agent teaming, unmanned vehicles, intelligent agents, trust in automation, operator workload, operator performance, usability, military operations, heterogeneous robot teams, trust calibration
- MeSH terms
- Adult, Artificial Intelligence, Automation, Computer Simulation, Female, Humans, Male, Man-Machine Systems, Task Performance and Analysis, User-Computer Interface, Young Adult
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