ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Human-Robot Interaction
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1557075
Self-Assessment in Machines Boosts Human Trust
Provisionally accepted
- HRL Laboratories (United States), Malibu, United States
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Low trust in autonomous systems remains a significant barrier to adoption and performance.To effectively increase trust in these systems, machines must perform actions to calibrate human trust based on an accurate assessment of both their capability and human trust in real time. Existing efforts demonstrate the value of trust calibration in improving team performance but overlook the importance of machine self-assessment capabilities in the trust calibration process. In our work, we develop a closed-loop trust calibration system for a human-machine collaboration task to classify images and demonstrate about 40% improvement in human trust and 5% improvement in team performance with trained machine self-assessment compared to the baseline, despite the same performance level between them. Our trust calibration system applies to any semi-autonomous application requiring human-machine collaboration.
Keywords: Machine Self-Assessment, Trust Calibration, Trust in AI, Autonomous Systems, Human-machine teaming
Received: 08 Jan 2025; Accepted: 28 Apr 2025.
Copyright: © 2025 Warmsley, Choudhary, Rego, Viani and Pilly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dana Warmsley, HRL Laboratories (United States), Malibu, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.