The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Watch Out for Updates: Understanding the Effects of Model Explanation Updates in AI-Assisted Decision Making
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Across multiple studies in the survey, a discrepancy between self-report and behavioural measures is observed.
“Across multiple studies in our overview a discrepancy between self-report and behavioural measures is observed (e.g., Papenmeier et al., 2022Papenmeier et al., , 2021;;Wang & Yin, 2023).”
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 Wang & Yin, 2023
Domain knowledge is an important factor that can influence the effect of explanations on users.
“Another important factor that can influence the effect of explanations on users is their domain knowledge (Wang & Yin, 2023).”
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 Wang & Yin, 2023
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Cites
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“gree of trust calibration in the AI model [87, 88, 91], and the decision making performance of the human-AI team [7, 16, 52, 56, 67].”
BackgroundInfluentialDoes the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
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Manipulating and Measuring Model Interpretability
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
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“, the fraction of tasks for which the participant’s final prediction agreed with the model’s prediction, among all tasks where the participant’s initial prediction disagreed with the model’s prediction), another metric commonly used in previous studies [36, 93, 95]11.”
ResultSelf-citationThe 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
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Self-citationExplaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders
Cheng, H.-F., Wang, R., Zhang, Z., O'Connell, F., Gray, T., Harper, F. M., & Zhu, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300789 · Cited by 292
Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff
Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012429 · Cited by 284
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De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221
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
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
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Dikmen, M., & Burns, C. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102792 · Cited by 116
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Human Decision Making with Machine Assistance: An Experiment on Bailing and Jailing
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Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers
Ghai, B., Liao, Q. V., Zhang, Y., Bellamy, R., & Mueller, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432934 · Cited by 106
Anik, A. I., & Bunt, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445736 · Cited by 103 · cited 1×
“To this end, one promising direction is to supplement explanations of AI models with explanations of the underlying data [4], which in e�ect may help people establish some “knowledge” or data-driven insights about the domain.”
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Second Chance for a First Impression? Trust Development in Intelligent System Interaction
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Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity
Carton, S., Mei, Q., & Resnick, P. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7282 · Cited by 58
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Metadata
- Authors
- Wang, X., & Yin, M.
- Affiliations
- Computer Science, Purdue University, United States
- ORCID
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-19
- Cited by
- 38
- References
- 23
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