选择基于AI的决策支持系统的决策因素 - - 预测中的任务委托案例
Kai Heinrich1, Christian Janiesch2, Oliver Krancher3
1Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
PloS one
|July 24, 2025
概括
选择合适的人工智能 (AI) 决策支持系统 (DSS) 对于机器维护预测至关重要. 性能,努力和透明度是关键因素,人工智能系统提供直接的剩余使用寿命估计和基于相似性的匹配是首选的.
科学领域:
- 决策支持系统 (DSS) 是指决策支持系统.
- 人工智能 (AI) 是一种人工智能.
- 机器维护预测 机器维护预测
背景情况:
- 组织寻求通过使用人工智能集成的DSS来提高决策性能和速度.
- 选择一个合适的基于AI的DSS是利用AI潜力的关键挑战.
- 机器维护预测是基于AI的DSS的一个关键应用领域.
研究的目的:
- 开发一个委托的视角来确定决定因素和AI系统特征影响基于AI的DSS选择.
- 使用分析层次过程来确定AI系统特征的决策权重.
- 探索用户专业知识对预测AI系统特征偏好的影响.
主要方法:
- 开发了一个委托的视角,以确定关键的决策因素和AI系统特征.
- 利用分析层次流程 (AHP) 来推导这些特征的决策权重.
- 应用了衍生权重来评估基于AI的DSS预后的三种原型.
主要成果:
- 绩效成为最关键的决策因素,其次是努力和透明度.
- 使用直接剩余使用寿命估计和基于相似性的匹配的AI预测系统最符合用户偏好.
- 新手和专业用户强烈喜欢视觉解释,而中度经验丰富的用户也重视技能发展的结构性解释.
结论:
- 基于人工智能的DSS选择机器维护预测应优先考虑性能,努力和透明度.
- 特定的AI原型,如直接剩余有用寿命估计和基于相似性的匹配,非常适合用户的需求.
- 将AI解释类型 (视觉与结构) 根据用户的专业知识水平进行定制,可以优化DSS的采用和有效性.
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