机器的自我评估增强了人类的信任。

IF 3 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-05-26 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1557075
Dana Warmsley, Krishna Choudhary, Jocelyn Rego, Emma Viani, Praveen K Pilly
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引用次数: 0

摘要

对自治系统的低信任度仍然是采用和性能的重大障碍。为了有效地增加对这些系统的信任,机器必须根据对其能力和人类信任的实时准确评估来执行行动来校准人类的信任。现有的研究证明了信任校准在提高团队绩效方面的价值,但忽视了机器自评估能力在信任校准过程中的重要性。在我们的工作中,我们为人机协作任务开发了一个闭环信任校准系统,用于对图像进行分类,并证明与基线相比,经过训练的机器自我评估的人类信任提高了约40%,团队绩效提高了5%,尽管它们之间的机器性能水平相同。我们的信任校准系统适用于任何需要人机协作的半自动应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-assessment in machines boosts human Trust.

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 machine performance level between them. Our trust calibration system applies to any semi-autonomous application requiring human-machine collaboration.

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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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