可解释的人工智能和回声状态网络对人机交互的信任进行校准
Sijia Hao1, Fei Teng2, Ruipeng Hou1
1School of Mental Health, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China.
Scientific reports
|January 6, 2026
概括
可解释的人工智能 (XAI) 显著提升了对人工智能系统的信任,特别是当人工智能决策失败并且用户获得合理性时. 来自卷积神经网络 (CNN) 的视觉解释可以提高理解,并有效地建立信任.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 信任对于人工智能性能至关重要,但由于"黑子"模型而具有挑战性.
- 可解释的人工智能 (XAI) 和信任校准是解决这一挑战的关键.
- 现有的方法往往缺乏透明度,阻碍了用户的信任.
研究的目的:
- 量化分析XAI和信任校准在人与人工智能交互中的作用.
- 检查AI可解释性和交互结果对信任的影响.
- 将XAI与精度,信任校准和用户满意度的最先进方法进行比较.
主要方法:
- 使用了一种2x2之间的实验设计.
- 使用了两个基准数据集 (CIFAR-10用于视觉,SQuAD用于文本).
- 使用了与XAI技术 (Grad-CAM,注意力机制) 集成的卷积神经网络 (CNN).
主要成果:
- 可解释的人工智能显著调节了信任水平,特别是在失败的互动和有理由的互动中.
- 基于CNN的解释通过视觉证据提高了理解和信任.
- 隐式信任指标揭示了显式自我报告无法捕捉到的动态.
- 人口统计学因素,如性别,并没有显著影响信任.
结论:
- 可解释性和动态信任校准对于发展对人工智能系统的信任至关重要.
- XAI方法,尤其是视觉方法,提高了用户的理解和信任.
- 这些发现支持在各个领域广泛采用人工智能系统.
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