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This paper presents an argument for why we are not measuring trust sufficiently in explainability, interpretability, and transparency research.
Quantifying Interpretability and Trust in Machine Learning Systems
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Conceptual and methodological considerations in the study of trust and suspicion1: Conceptual Considerations Methodological Considerations Summary and Conclusions REFERENCES
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Does projection into use improve trust and exploration? An example with a cruise control system
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Trust and reliance on an automated combat identification system
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Alon Jacovi, Ana Marasovic, Tim Miller, and Yoav Goldberg. 2021 · 2010
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Measuring trust in supply chain partners’ relationships
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Measuring trust in organisational research: Review and recommendations
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Metrics for explainable AI: Challenges and prospects
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Incentivizing Creativity: A Large-Scale Experiment with Performance Bonuses and Gifts
Christiane Bradler, Susanne Neckermann, and Arne Jonas Warnke. 2019 · 2019
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On measuring trust and distrust in journalism: Reflection of the status quo and suggestions for the road ahead
Katherine M Engelke, Valerie Hase, and Florian Wintterlin. 2019 · 2019
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Trust mediating reliability–reliance relationship in supervisory control of human–swarm interactions
Aya Hussein, Sondoss Elsawah, and Hussein A Abbass. 2020 · 2020
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Building And Measuring Trust In Human-Machine Systems. In 2021 IEEE International Conference on Autonomous Systems (ICAS) . 1–5
Lida Ghaemi Dizaji and Yaoping Hu. 2021 · 2021
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Trust among supply chain partners: a review
Gaurav Tejpal, R K Garg, and Anish Sachdeva. 2013 · 2013
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Two sides of the coin: measuring and communicating the trustworthiness of online information
Jason R C Nurse, Ioannis Agrafiotis, Michael Goldsmith, Sadie Creese, and Koen Lamberts. 2014 · 2014
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Behavioral Measurement of Trust in Automation: The Trust Fall
David Miller, Mishel Johns, Brian Mok, Nikhil Gowda, David Sirkin, Key Lee, and Wendy Ju. 2016 · 2016
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A taxonomy of emergent trusting in the human–machine relationship
Robert R Hoffman. 2017 · 2017
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Play for Real(ism) - Using Games to Predict Human-AI interactions in the Real World
Rotem D Guttman, Jessica Hammer, Erik Harpstead, and Carol J Smith. 2021 · 2021
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IMPACTS: a trust model for human-autonomy teaming
Ming Hou, Geoffrey Ho, and David Dunwoody. 2021 · 2021
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Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps
Tobias Huber, Katharina Weitz, Elisabeth André, and Ofra Amir. 2021 · 2021
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AI Trust Score: A User-Centered Approach to Building, Designing, and Measuring the Success of Intelligent Workplace Features. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI EA ’21, Article 54) . Association for Computing Machinery, New York, NY, USA, 1–7
Jennifer Wang and Angela Moulden. 2021 · 2021
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