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Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant

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Designing Interactive Transfer Learning Tools for ML Non-Experts

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Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

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From ”Explainable AI” to ”Graspable AI”

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Toward Foraging for Understanding of StarCraft Agents

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"I Want To See How Smart This AI Really Is": Player Mental Model Development of an Adversarial AI Player

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Capturing Humans’ Mental Models of AI: An Item Response Theory Approach

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Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study

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Is My Siri the Same as Your Siri? An Exploration of Users’ Mental Model of Virtual Personal Assistants, Implications for Trust

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Exploring the Role of Local and Global Explanations in Recommender Systems

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Intuitions, Analytics, and Killing Ants: Inference Literacy of High School-educated Adults in the {US}

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A Clinical Decision Support System for the Prediction of Quality of Life in ALS

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I Don’t Know, Is AI Also Used in Airbags?

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Tangible Explainable AI - an Initial Conceptual Framework

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Crowdsourcing and Evaluating Concept-driven Explanations of Machine Learning Models

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A Scoping Review of Mental Model Research in HCI from 2010 to 2021

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Understanding and Supporting Knowledge Decomposition for Machine Teaching

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Building Appropriate Mental Models: What Users Know and Want to Know about an Agentic AI Chatbot

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Color for Characters - Effects of Visual Explanations of AI on Trust and Observability

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Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles

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Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator ExperiencePreprint

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Improved Explanatory Efficacy on Human Affect and Workload Through Interactive Process in Artificial Intelligence

Kim, B. H., Koh, S., Huh, S., Jo, S., & Choi, S. (2020) · IEEE Access · doi:10.1109/access.2020.3032056 · Cited by 18 · see citing works

Investigating the intelligibility of a computer vision system for blind users

Ahmed, S., Balasubramanian, H., Stumpf, S., Morrison, C. P., Sellen, A. J., & Grayson, M. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377508 · Cited by 17 · see citing works

Does the User Have A Theory of the Recommender? A Grounded Theory Study

Ghori, M. M., Dehpanah, A., Gemmell, J. F., Qahri‐Saremi, H., & Mobasher, B. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3511047.3537680 · Cited by 16 · see citing works

How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge Outcomes

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Preferences for AI Explanations Based on Cognitive Style and Socio-Cultural Factors

Kopecka, H., Such, J. M., & Flucke, G. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637386 · Cited by 15 · see citing works

Trust in the Danger Zone: Individual Differences in Confidence in Robot Threat Assessments

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How Prevalent Is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses

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Keeping it "organized and logical"

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Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning Platforms

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Enhancing Analysts' Mental Models for Improving Requirements Elicitation: A Two-stage Theoretical Framework and Empirical Results

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Exploring Tangible Algorithmic Imaginaries in Movie Recommendations

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Identifying Group-Specific Mental Models of Recommender Systems: A Novel Quantitative Approach

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How it works

Tullio, J., Dey, A. K., Chalecki, J., & Fogarty, J. A. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240630 · Cited by 137 · see citing works

Unpacking Older Adults' Mental Models of Video Recommender Systems: A Qualitative Study

Zhang, Y., & Liu, J. (2024) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3677389.3702577

How it Works: A Field Study of Non-Technical Users Interacting with an Intelligent System

Tullio, J., Dey, A. K., Chalecki, J., & Fogarty, J. (2007)

Does the User Have A Theory of the Recommender? A Grounded Theory Study

Ghori, M. M., Dehpanah, A., Gemmell, J. F., Qahri‐Saremi, H., & Mobasher, B. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3511047.3537680 · Cited by 16 · see citing works