The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise
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While domain expertise does impact user trust, domain expertise alone cannot fully predict people's trust in a model because expectations and personal experiences play a significant role.
“However, while domain expertise does impact user trust (Ooge & Verbert, 2021) observe that as expectations and personal experiences play a significant role, domain expertise alone cannot fully predict people's trust in a model.”
From Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (2025) · cites this paper as Ooge & Verbert, 2021
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Foundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799
The role of trust in automation reliance
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00038-7 · Cited by 1,276
How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec, R. F. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858402 · Cited by 615
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300509 · Cited by 527
The effects of transparency on trust in and acceptance of a content-based art recommender
Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9051-3 · Cited by 448
The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
Bussone, A., Stumpf, S., & O'Sullivan, D. (2015) · 2015 International Conference on Healthcare Informatics · doi:10.1109/ichi.2015.26 · Cited by 441
How do visual explanations foster end users' appropriate trust in machine learning?
Yang, F., Huang, Z., Scholtz, J., & Arendt, D. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377480 · Cited by 200
Nourani, M., King, J., & Ragan, E. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7469 · Cited by 116
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Research works printed but not linked: 26
- 10.1016/j.ergon.2004.03.005 · DOI
- 10.1109/TVCG.2015.2467591 · DOI
- 10.1145/2939672.2939778 · DOI · Paper page
- 10.1007/978-3-319-68059-0_2 · DOI · Paper page
- 10.1007/978-3-319-68059-0_3 · DOI
- 10.1080/0144929X.2019.1656779 · DOI · Paper page
- 10.1609/aimag.v40i2.2850 · DOI · Paper page
- 10.1016/j.compag.2019.05.053 · DOI
- 10.1109/TREX51495.2020.00006 · DOI
- 10.1177/0018720814547570 · DOI · Paper page
- 10.1109/MIS.2013.24 · DOI
- 10.1109/TVCG.2018.2843369 · DOI · Paper page
- 10.1145/2856767.2856811 · DOI
- 10.1145/3334480.3382967 · DOI
- 10.1111/cgf.14034 · DOI
- 10.1109/TREX51495.2020.00005 · DOI
- 10.1109/TVCG.2016.2598544 · DOI
- 10.1016/B978-0-12-385522-0.00005-6 · DOI · Paper page
- 10.1111/cgf.13092 · DOI
- 10.1037/13620-004 · DOI · Paper page
- 10.1111/cgf.13210 · DOI
- 10.1145/3196490 · DOI
- 10.1002/hfm.20021 · DOI
- madsen, 2000, Measuring human-computer trust, 11th Australasian Conf Information Systems, 53, 6
- mohseni, 2020, Trust Evolution Over Time in Explainable AI for Fake News Detection, Fair & Responsible AI Workshop at CHI 2020
- 10.2312/TRVIS.20191187 · DOI
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Visser, R., Peters, T. M., Scharlau, I., & Hammer, B. (2025) · Cognitive Systems Research · doi:10.1016/j.cogsys.2025.101357 · Cited by 58
Metadata
- Authors
- Ooge, J., & Verbert, K.
- Venue
- 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 8-13
- Cited by
- 7
- References
- 8
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