The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement
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“While this pattern has been commonly observed in humans interacting with conversational agents (Jiang et al., 2022;Gupta et al., 2022), it is not only limited to traditional conversational interfaces (Gomez et al., 2023).”
From Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review (2025) · cites this paper as Gomez et al., 2023
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“However, such exchange of information does not necessarily rely on a conversational interface, as demonstrated in the evaluation of an AI system that could constantly provide guidance on a decision sub-problem for identifying bird categories (Gomez et al., 2023).”
From Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review (2025) · cites this paper as Gomez et al., 2023
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Cites
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
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018) · Management Science · doi:10.1287/mnsc.2016.2643 · Cited by 1,230
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124
Power to the People: The Role of Humans in Interactive Machine Learning
Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014) · AI Magazine · doi:10.1609/aimag.v35i4.2513 · Cited by 1,084
Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2018) · arXiv · doi:10.48550/arxiv.1811.11839 · Cited by 783
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2020) · arXiv · doi:10.48550/arxiv.2006.14779 · Cited by 754
Do as AI say: susceptibility in deployment of clinical decision-aids
Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00385-9 · Cited by 543
Research works printed but not linked: 13
- Berlyand, 2018, How artificial intelligence could transform emergency department operations, Am. J. Emerg. Med., 36, 1515, 10.1016/j.ajem.2018.01.017 · DOI
- Chavaillaz, 2019, Expertise, automation and trust in X-ray screening of cabin baggage, Front. Psychol., 10, 256, 10.3389/fpsyg.2019.00256 · DOI
- Chiang, Chun-Wei, Yin, Ming, 2021. You’d Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift. In: 13th ACM Web Science Conference 2021. pp. 120–129. · DOI · Paper page
- Diao, 2021, Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes, Nature Commun., 12, 1, 10.1038/s41467-021-21896-9 · DOI · Paper page
- Holzinger, 2022, Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence, Inf. Fusion, 79, 263, 10.1016/j.inffus.2021.10.007 · DOI
- Liao, Q. Vera, Gruen, Daniel, Miller, Sarah, 2020. Questioning the AI: informing design practices for explainable AI user experiences. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. pp. 1–15. · DOI · Paper page
- Lu, Zhuoran, Yin, Ming, 2021. Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. pp. 1–16. · DOI · Paper page
- Ngo, Thao, Kunkel, Johannes, Ziegler, Jürgen, 2020. Exploring mental models for transparent and controllable recommender systems: a qualitative study. In: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. pp. 183–191. · DOI
- Rastogi, 2022, Deciding fast and slow: The role of cognitive biases in AI-assisted decision-making, Proc. ACM Hum.-Comput. Interact., 6, 10.1145/3512930 · DOI · Paper page
- Ribeiro, Marco Tulio, Singh, Sameer, Guestrin, Carlos, 2016. “Why should I trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1135–1144. · DOI · Paper page
- Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y · DOI · Paper page
- Schaffer, James, O’Donovan, John, Michaelis, James, Raglin, Adrienne, Höllerer, Tobias, 2019. I can do better than your AI: expertise and explanations. In: Proceedings of the 24th International Conference on Intelligent User Interfaces. pp. 240–251. · DOI · Paper page
- Zhang, Qiaoning, Lee, Matthew L., Carter, Scott, 2022. You Complete Me: Human-AI Teams and Complementary Expertise. In: CHI Conference on Human Factors in Computing Systems. pp. 1–28. · DOI · Paper page
Cited by
Slimi, Z., & Villarejo Carballido, B. (2023) · TEM Journal · doi:10.18421/tem122-02 · Cited by 212
Gomez, C., Cho, S. M., Ke, S., Huang, C.-M., & Unberath, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1521066 · Cited by 209
Self-citationHow Time Pressure in Different Phases of Decision-Making Influences Human-AI Collaboration
Cao, S., Gomez, C., & Huang, C.-M. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610068 · Cited by 48
Self-citationExplainable AI decision support improves accuracy during telehealth strep throat screening
Gomez, C., Smith, B.-L., Zayas, A., Unberath, M., & Canares, T. (2024) · Communications Medicine · doi:10.1038/s43856-024-00568-x · Cited by 30
Self-citationAI or human? A study of university students’ awareness of library reference service agents
Wang, D., Guo, J., Zheng, K., & Deng, X. (2026) · The Electronic Library · doi:10.1108/el-06-2025-0230 · Cited by 2
Metadata
- Authors
- Gomez, C., Unberath, M., & Huang, C.-M.
- Venue
- International Journal of Human-Computer Studies
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 172
- Pages
- 102977
- ISSN
- 1071-5819
- Cited by
- 50
- References
- 35
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor)
- Funders
- National Science Foundation (1840088)
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- Gomez, C., Unberath, M., & Huang, C.-M. (2023). Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement. International Journal of Human-Computer Studies. https://doi.org/10.1016/j.ijhcs.2022.102977
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.