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机器学习错误对人类决策的影响:对模型准确度,错误类型和错误重要性的操纵
Laura E Matzen1, Zoe N Gastelum2, Breannan C Howell2
1Sandia National Laboratories, Mail Stop 1327, P.O. Box 5800, Albuquerque, NM, 87185-1327, USA. lematze@sandia.gov.
Cognitive research: principles and implications
|August 25, 2024
概括
人类在对象检测任务中的表现受到机器学习 (ML) 模型准确性的影响. 参与者很难识别ML模型错过了更多的错误报警,特别是高精度模型.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 机器学习评估 机器学习评估
背景情况:
- 机器学习 (ML) 模型越来越多地用于支持人类在各种任务中的决策.
- 了解人类如何与ML输出 (特别是错误) 互动和受到影响,对于有效部署至关重要.
- 之前的研究已经探索了人类-人工智能协作,但不同类型的ML错误的特定认知影响需要进一步调查.
研究的目的:
- 研究正确和不正确的机器学习 (ML) 输出对人类在对象检测任务中的表现的认知影响.
- 确定不同的ML模型准确性和错误类型如何影响人类识别目标和模型错误的能力.
- 评估任务框架和错误重要性对人类检测ML错误的影响.
主要方法:
- 使用T和L物体检测任务 (在L形分心物中识别T形目标) 进行了五项实验.
- 参与者使用和不使用ML模型输出 (边界框) 执行任务.
- 实验操纵了ML输出准确度,错误比例 (错误与错误报警),错误重要性和任务框架 (人类与模型性能重点).
主要成果:
- 模型错误对于参与者来说要比模型错误报警要难得多.
- 人类的表现通常随着ML模型准确度的提高而改善,但参与者在高度准确的模型中更频繁地忽视了错误.
- 关于特定错误类型的明确警告对参与者的表现的影响最小.
结论:
- 人类认知处理显著影响机器学习 (ML) 模型输出的解释和影响.
- 机器学习错误的类型 (错误与错误报警) 对人类检测能力有不同的影响.
- 可接受的ML性能水平和错误类型必须考虑人类的认知局限性和任务上下文,以实现最佳的人类-AI协作.
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