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Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation in the AI context.
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Validated Questionnaires Should not be Modified
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Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 78, 16 pages
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Manipulating and Measuring Model Interpretability. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 237, 52 pages
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Fifty Shades of Grey: In Praise of a Nuanced Approach Towards Trustworthy Design. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21) . Association for Computing Machinery, New York, NY, USA, 64–76
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How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies
Oleksandra Vereschak, Gilles Bailly, and Baptiste Caramiaux. 2021 · 2021
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“Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design
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The Value of Measuring Trust in AI-A Socio-Technical System Perspective
Michaela Benk, Suzanne Tolmeijer, Florian von Wangenheim, and Andrea Ferrario. 2022 · 2022
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Towards a Generalized Scale to Measure Situational Trust in AI Systems. In CHI 2022 TRAIT Workshop on Trust and Reliance in AI-Human Teams (Virtual/New Orleans, LA, USA). Association for Computing Machinery, New York, NY, USA, 9 pages
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Trust and Reliance in XAI–Distinguishing Between Attitudinal and Behavioral Measures
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A Micro and Macro Perspective on Trustworthiness: Theoretical Underpinnings of the Trustworthiness Assessment Model (TrAM)
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Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22) . Association for Computing Machinery, New York, NY, USA, Article 254, 7 pages
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Robert R. Hoffman, Shane T. Mueller, Gary Klein, and Jordan Litman. 2023 · 2023
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Trust Issues with Trust Scales: Examining the Psychometric Quality of Trust Measures in the Context of AI. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23) . Association for Computing Machinery, New York, NY, USA, 7 pages
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Development and Validation of a Positive-Item Version of the Visual Aesthetics of Websites Inventory: The VisAWI-Pos
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The Importance of Distrust in AI. In Explainable Artificial Intelligence , Luca Longo (Ed.). Springer Nature, Cham, Switzerland, 301–317
Tobias M. Peters and Roel W. Visser. 2023 · 2023
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Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 248–260
Nicolas Scharowski, Michaela Benk, Swen J. Kühne, Léane Wettstein, and Florian Brühlmann. 2023 · 2023
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Nicolas Scharowski and Sebastian A. C. Perrig. 2023 · 2023
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Magdalena Wischnewski, Nicole Krämer, and Emmanuel Müller. 2023 · 2023
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