The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A Survey on Trust Modeling
Open accessAbstract
The concept of trust and/or trust management has received considerable attention in engineering research communities as trust is perceived as the basis for decision making in many contexts and the motivation for maintaining long-term relationships based on cooperation and collaboration. Even if substantial research effort has been dedicated to addressing trust-based mechanisms or trust metrics (or computation) in diverse contexts, prior work has not clearly solved the issue of how to model and quantify trust with sufficient detail and context-based adequateness. The issue of trust quantification has become more complicated as we have the need to derive trust from complex, composite networks that may involve four distinct layers of communication protocols, information exchange, social interactions, and cognitive motivations. In addition, the diverse application domains require different aspects of trust for decision making such as emotional, logical, and relational trust. This survey aims to outline the foundations of trust models for applications in these contexts in terms of the concept of trust, trust assessment, trust constructs, trust scales, trust properties, trust formulation, and applications of trust. We discuss how different components of trust can be mapped to different layers of a complex, composite network; applicability of trust metrics and models; research challenges; and future work directions.
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Cites
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Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems
Andras, P., Esterle, L., Guckert, M., Han, T. A., Lewis, P. R., Milanovic, K., Payne, T., Perret, C., Pitt, J., Powers, S. T., et al. (2018) · IEEE Technology and Society Magazine · doi:10.1109/mts.2018.2876107 · Cited by 153
A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives
Gebru, B., Zeleke, L., Blankson, D., Nabil, M., Nateghi, S., Homaifar, A., & Tunstel, E. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3144956 · Cited by 101
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Toward the dynamic relationship between AI transparency and trust in AI: A case study on ChatGPT
Lee, C., & Cha, K. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2405266 · Cited by 53
Persuading Patients Using Rhetoric to Improve Artificial Intelligence Adoption: Experimental Study
Sebastian, G., George, A., & Jackson Jr, G. (2023) · Journal of Medical Internet Research · doi:10.2196/41430 · Cited by 48
“Trust, a psychological construct that encompasses an emotional and a logical aspect [47], can influence a user’s perception of using an AI product.”
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McMahon, G., Akash, K., Reid, T., & Jain, N. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.04.113 · Cited by 10
Metadata
- Authors
- Cho, J.-H., Chan, K., & Adali, S.
- Venue
- ACM Computing Surveys
- Published
- 2015-10-12
- Volume
- 48
- Issue
- 2
- Pages
- 1-40
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2135363691
- Cited by
- 342
- References
- 14
- FWCI
- 59.80
- Type
- journal article
- Language
- en
- Open access
- bronze
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