Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
Open accessAbstract
ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people’s trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people’s trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.
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On image recognition tasks, people will trust an incorrect machine decision, even if they would have made the correct decision on their own.
“On image recognition tasks, Suresh et al. found that people will trust an incorrect machine decision, even if they would have made the correct decision on their own [48].”
From Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021) · cites this paper as [48]
Humans may develop a misplaced trust in automation, which can degrade their output.
“If not, the human may develop a misplaced trust in the automation, which could have the adverse efect of degrading their output, as has been demonstrated in prior work[48].”
From Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021) · cites this paper as [48]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.70 · Cited by 2,356 · see citing works
Algorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061 · see citing works
Fairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631 · see citing works
When is an illustration worth ten thousand words?
Mayer, R. E., & Gallini, J. K. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.4.715 · Cited by 1,051 · cited 1× · see citing works
“os as an instructional tool has been shown to significantly improve recall of conceptual information and creative problem solving over other mediums, particular for lower prior-knowledge participants [25]. Additionally, the video medium allowed us to track the amount of information the participant received through vie…”
BackgroundComposition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S. A., Rajpoot, N., & Shah, M. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_33 · Cited by 891 · see citing works
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 · see citing works
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 · cited 4× · see citing works
“While a growing body of research focuses on issues of trust in ML, and more recently on some aspects of system transparency [24, 39], to our knowledge this is the first study characterizing trust with respect to both prior knowledge as well as system transparency.”
BackgroundResultInfluentialThe role of transparency in recommender systems
Sinha, R. R., & Swearingen, K. (2002) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/506443.506619 · Cited by 480 · see citing works
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 · see citing works
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 · see citing works
Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377 · see citing works
The relative influence of advice from human experts and statistical methods on forecast adjustments
Önkal, D., Goodwin, P., Thomson, M. E., Gonul, M. S., & Pollock, A. C. (2009) · Journal of Behavioral Decision Making · doi:10.1002/bdm.637 · Cited by 331 · see citing works
Accountability and automation bias
SKITKA, L. J., MOSIER, K., & BURDICK, M. D. (2000) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1999.0349 · Cited by 324 · see citing works
Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments
Green, B., & Chen, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287563 · Cited by 275 · see citing works
Assessing Risk Assessment in Action
Stevenson, M. T. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3016088 · Cited by 195 · see citing works
Jakesch, M., French, M., Ma, X., Hancock, J. T., & Naaman, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300469 · Cited by 190 · see citing works
An Evaluation of the Human-Interpretability of Explanation
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., & Doshi-Velez, F. (2019) · arXiv · doi:10.48550/arxiv.1902.00006 · Cited by 125 · see citing works
Can You Teach Me To Machine Learn?
Sulmont, E., Patitsas, E., & Cooperstock, J. R. (2019) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3287324.3287392 · Cited by 83 · see citing works
Povyakalo, A. A., Alberdi, E., Strigini, L., & Ayton, P. (2013) · Medical Decision Making · doi:10.1177/0272989x12465490 · Cited by 79 · see citing works
Lao, N., Lee, I. A., & Abelson, H. (2019) · ICERI proceedings · doi:10.21125/iceri.2019.0137 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 12
- Ifeoma Ajunwa. 2016. The Paradox of Automation as Anti-Bias Intervention. Forthcoming in Cardozo Law Review (2016).
- Adrian Bussone, Simone Stumpf, and Dympna O'Sullivan. 2015. The role of explanations on trust and reliance in clinical decision support systems. In 2015 International Conference on Healthcare Informatics. IEEE, 160-169.
- Erin Collins. 2018. Punishing Risk. Geo. LJ 107 (2018), 57.
- Dominik Dellermann, Adrian Calma, Nikolaus Lipusch, Thorsten Weber, Sascha Weigel, and Philipp Ebel. 2019. The Future of Human-AI Collaboration: A Taxon- omy of Design Knowledge for Hybrid Intelligence Systems. In Hawaii Interna- tional Conference on System Sciences (HICSS). Maui, Hawaii, USA. http://pubs.wi- kassel.de/wp-content/uploads/2018/09/JML_706.pdf
- Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015. Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General 144, 1 (2015), 114.
- Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. 2017. Artificial intelligence in health- care: past, present and future. Stroke and vascular neurology 2, 4 (2017), 230-243.
- John D Lee and Katrina A See. 2004. Trust in automation: Designing for appro- priate reliance. Human factors 46, 1 (2004), 50-80.
- Weizhe Liu, Krzysztof Maciej Lis, Mathieu Salzmann, and Pascal Fua. 2019. Geo- metric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
- Weizhe Liu, Mathieu Salzmann, and Pascal Fua. 2019. Context-Aware Crowd Counting. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wort- man Vaughan, and Hanna Wallach. 2018. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018). · arXiv
- Nava Tintarev and Judith Masthoff. 2015. Explaining recommendations: Design and evaluation. In Recommender systems handbook. Springer, 353-382.
- Michael Yeomans, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. 2017. Making sense of recommendations. Journal of Behavioral Decision Making (2017).
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- Authors
- Suresh, H., Lao, N., & Liccardi, I.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- 12th ACM Conference on Web Science
- Publisher
- ACM
- Published
- 2020-07-06
- Pages
- 315-324
- OpenAlex
- W3027367391
- Cited by
- 49
- References
- 20
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 7 · 2023: 6 · 2022: 6 · 2021: 3
- FWCI
- 5.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Web Science Conference (WebSci) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- William and Flora Hewlett Foundation
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- trust in machine learning, human-AI decision-making, human expertise, system information, confidence measures
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