Exploring the Effects of Machine Learning Literacy Interventions on Laypeople’s Reliance on Machine Learning Models
Open accessAbstract
Today, machine learning (ML) technologies have penetrated almost every aspect of people’s lives, yet public understandings of these technologies are often limited. This highlights the urgent need of designing effective methods to increase people’s machine learning literacy, as the lack of relevant knowledge may result in people’s inappropriate usage of machine learning technologies. In this paper, we focus on an ML-assisted decision-making setting and conduct a human-subject randomized experiment to explore how providing different types of user tutorials as the machine learning literacy interventions can influence laypeople’s reliance on ML models, on both in-distribution and out-of-distribution examples. We vary the existence, interactivity and scope of the user tutorial across different treatments in our experiment. Our results show that user tutorials, when presented in appropriate forms, can help some people rely on ML models more appropriately. For example, for those individuals who have relatively high ability in solving the decision-making task themselves, receiving a user tutorial that is interactive and addresses the specific ML model to be used allows them to reduce their over-reliance on the ML model when they could outperform the model. In contrast, low-performing individuals’ reliance on the ML model is not affected by the presence or the type of user tutorial. Finally, we also find that people perceive the interactive tutorial to be more understandable and slightly more useful. We conclude by discussing the design implications of our study.
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Tasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval, recidivism prediction, house price estimation, and student admission.
“Tasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval [27, 34, 124], recidivism prediction [31, 47, 86], house price estimation [2, 13, 25], and student admission [13, 24].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [25]
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Raees, M., & Papagelis, K. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798834 · Cited by 1 · see citing works
Leffrang, D., Passlack, N., Müller, O., & Posegga, O. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-026-01000-5 · Cited by 3 · see citing works
“Subjective Biased Perception on AI Knowledge and AI Aversion”
Oezguer, K., Jan, S., & Thomas, L. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00070
The Dynamics of Human-AI Interactions in Organizational Decision-Making
Öz, B., Karran, A. J., Beringer, J., Coursaris, C. K., & Léger, P. (2025) · Progress in IS · doi:10.1007/978-3-031-83512-4_3
Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasets
Boháček, M., & Echávarri, I. V. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758141
Xiao, X., & Cheng, Y. (2026) · ACM Transactions on Computing Education · doi:10.1145/3803805
Understanding and Designing Human-Centered AI Interactions for Non-Expert Users
Raees, M. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799192
On the waves of change: the burden of knowledge and strategic human capital
El-Zayaty, A., & Ganco, M. (2026) · Strategic Management Review · doi:10.1108/smr-07-2022-0867
Li, Z.-Y., Ma, Z.-Z., He, S.-S., Lu, Z.-R., Xiao, Z., & Yin, M. (2026) · Proceedings of the 2026 ACM Conference on Human-AI Complementarity and Alignment · doi:10.1145/3834580.3838738
Self-citationArmstrong, B. A., Cheuy, L., Kottler, N. E., & Larson, D. B. (2026) · American Journal of Roentgenology · doi:10.2214/ajr.26.35826
- Authors
- Chiang, C.-W., & Yin, M.
- Affiliations
- Purdue University West Lafayette (US)
- ORCID
- Yin, Ming
- Venue
- 27th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2022-03-22
- Pages
- 148-161
- OpenAlex
- W4221056569
- Cited by
- 42
- References
- 54
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 10 · 2023: 10 · 2022: 1
- FWCI
- 4.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion, cc-by)
- Funders
- National Science Foundation (IIS-185033)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- overreliance on AI, user tutorial, interactive tutorials, laypeople, individual differences in performance, public understanding of AI, tutorial design
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