更多并不总是更好:人工智能产生的信心的影响和人类与自动化交互的解释
Shihong Ling1, Yutong Zhang1, Na Du1
1University of Pittsburgh, USA.
Human factors
|March 4, 2024
概括
视觉解释显著改善自主系统的性能,信任和用户偏好. 自信可视化本身并没有影响信任或偏好,强调了定制解释对有效的人机协作的重要性.
科学领域:
- 人与计算机的互动.
- 人工智能的人工智能是人工智能.
- 自动化系统自动化系统
背景情况:
- 系统透明度对于自主系统的信任和任务成功至关重要.
- 关于信任信息和解释影响的先前研究产生了不同的结果.
- 现有的研究经常使用手动解释,限制了可扩展性和现实世界的适用性.
研究的目的:
- 在自主系统中自动生成和可视化信任和解释.
- 评估这些可视化对表现,信任,偏好和眼睛跟踪行为的影响.
- 为设计透明的自动化系统提供指导.
主要方法:
- 对42名大学生进行了一项双重任务实验,他们使用模拟监控测试台.
- 一个2x3混合设计评估了信心可视化 (是/否) 和视觉解释类型 (没有,边界框,边界框和关键点).
- 评估的指标包括任务性能,人类信任,偏好和眼睛跟踪行为.
主要成果:
- 视觉解释 (边界框和关键点) 在缺乏信心的情况下提高了检测性能.
- 视觉解释提高了对智能探测器的信任和偏好,无论解释类型如何.
- 自信可视化没有影响人类的信任或偏好; 视觉信息减缓了冲击速度.
结论:
- 视觉解释可以提高人机交互的性能,信任和偏好,部分是通过改变搜索策略.
- 仅靠对自信的可视化,就不足以提高信任或偏好.
- 适合上下文,以用户为中心的解释是有效的人机协作的关键,尽管过多的信息可能是有害的.
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