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Cited by 50

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

Gomez, Catalina, Unberath, Mathias, Huang, Chien-Ming·2023·International Journal of Human-Computer Studies
DOI 10.1016/j.ijhcs.2022.102977
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Research works printed but not linked: 13

  1. Berlyand, 2018, How artificial intelligence could transform emergency department operations, Am. J. Emerg. Med., 36, 1515, 10.1016/j.ajem.2018.01.017 · DOI
  2. Chavaillaz, 2019, Expertise, automation and trust in X-ray screening of cabin baggage, Front. Psychol., 10, 256, 10.3389/fpsyg.2019.00256 · DOI
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y · DOI · Paper page
  12. 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
  13. 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

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Metadata

Authors
Gomez, C., Unberath, M., & Huang, C.-M.
ORCID
Venue
International Journal of Human-Computer Studies
Publisher
Elsevier BV
Published
2023-04
Volume
172
Pages
102977
ISSN
1071-5819
DOI
10.1016/j.ijhcs.2022.102977
Cited by
50
References
35
Language
en
Licence
Funders
National Science Foundation (1840088)

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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.