The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Abstract
What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions.
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“People decide when to rely on an AI by using their mental models [3, 30], or internal representations, of how the AI tends to behave: when it is most accurate, when it is most likely to fail, etc.”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as [30]
Citations
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Metadata
- Authors
- Kulesza, T., Stumpf, S., Burnett, M., & Kwan, I.
- Affiliations
- Oregon State University (US)
- City, University of London (GB)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2012-05-05
- Pages
- 1-10
- OpenAlex
- W2168747479
- Cited by
- 225
- References
- 31
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 22 · 2023: 25 · 2022: 24 · 2021: 24 · 2020: 35 · 2019: 16 · 2018: 14 · 2017: 6 · 2016: 9 · 2015: 5 · 2014: 3 · 2013: 1 · 2012: 1
- FWCI
- 2.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Funders
- National Science Foundation (0803487)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- mental models, intelligent agents, recommender systems, music recommendation, personalization, user satisfaction, system transparency
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