Calibrating Reliance on Automated Advice: Transparency and Trust Calibration Feedback.
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| Title: | Calibrating Reliance on Automated Advice: Transparency and Trust Calibration Feedback. |
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| Authors: | Tatasciore, Monica (AUTHOR), Loft, Shayne (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Dec2025, Vol. 41 Issue 23, p14723-14733. 11p. |
| Subjects: | Decision making, Decision support systems, Statistical reliability, User-centered system design, Psychological factors, Disclosure |
| Abstract: | Inappropriate reliance on automated advice can result in humans accepting incorrect or rejecting correct advice. Increased automation transparency and trust calibration feedback are principles purported to promote accurate automation use. We examined the effects of automation transparency, trust calibration feedback, and their potential interacting effect on automation use accuracy and other outcomes. Participants completed uninhabited vehicle management missions by agreeing/disagreeing with automated advice. Transparency was manipulated within-subjects (low, high) and trust calibration feedback between-subjects (absent, present). If trust was inappropriate, trust calibration feedback instructed participants to take their time and carefully check display information. Higher transparency benefited automation use accuracy, decision time, perceived workload, trust, and usability. Trust calibration feedback had no benefit on automation use accuracy and subsequently did not amplify the benefits of increased transparency. These findings have potential implications to inform the design of automated decision aids to support human understanding of, and calibration to, automation capabilities. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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