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"你对此有没有确定?" 校准分类模型任务准确性和信心对可信度,信任和绩效的影响
August Capiola1, Krista N Harris2, Gene M Alarcon1
1Air Force Research Laboratory, 2215 First St., WPAFB, OH, 45433, USA.
Applied ergonomics
|November 16, 2025
概括
了解人工智能 (AI) 和机器学习 (ML) 是非常重要的. 这项研究表明,具有高可信度但精度较低的AI模型被认为不那么值得信赖,影响用户的信任和性能.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 认知心理学 认知心理学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越普遍.
- 确保用户对AI/ML系统能力的预期进行校准对于适当的依赖至关重要.
- 传统上不透明的AI模型正在发展,以更好地传达信心和准确性.
研究的目的:
- 调查AI模型分类的信心和准确性如何影响用户的信任和任务性能.
- 扩大现有研究,以了解模型信心,准确性和与信任相关的结果之间的相互作用.
主要方法:
- 使用了对象内部的实验设计.
- 参与者从事在线图像分类任务,使用不同程度的信心和准确度的AI模型.
- 测量了与信任相关的标准,任务绩效和决策时间.
主要成果:
- 模型准确度对用户感知的影响在很大程度上取决于模型报告的信心.
- 人工智能模型表现出高信心与低准确度相结合的模型通常被认为不那么值得信赖.
- 这些不那么值得信赖的模型导致用户信任度下降,任务性能降低,决策时间增加.
结论:
- 模型信心是用户如何感知和信任人工智能系统的关键因素,特别是当准确性较低时.
- 调查结果强调了透明地传达人工智能模型的信心和准确性的重要性,以促进校准的依赖.
- 这项研究加强了先前的研究,并强调了设计可靠的人工智能接口的含义.
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