人工智能解释对信任和依赖的影响:一项关于工作坊调度的研究
Till Saßmannshausen1, Peter Burggräf1, Marc Hassenzahl2
1International Production Engineering and Management, University of Siegen, Siegen, Germany.
Ergonomics
|March 11, 2026
概括
对人工智能调度器的简短解释增加了依赖,但不一定是信任. 简短的理由充当能力线索,在没有深入理解的情况下促进采用. 校准的信任需要对AI限制的指导.
科学领域:
- 人与计算机的交互
- 人工智能在管理中的作用
- 运营研究 运营研究
背景情况:
- 信任校准对于生产管理中有效的人类-人工智能协作至关重要.
- 简短的解释对黑盒人工智能调度器的信任和依赖的影响尚不清楚.
研究的目的:
- 调查深度强化学习 (DRL) 调度器中的自然语言推理如何与透明的启发式学习相比影响信任和依赖.
- 确定解释是否影响态度信任或感知能力,以及任务难度和用户专业知识如何调节这些影响.
主要方法:
- 253名专业人员和研究生工程师在灵活的工作车间安排 (FJSS) 中进行了在线实验.
- 将DRL调度器与理性的比较与先入先出 (FIFO) 启发式,确保相同的建议.
- 一个五步,路径依赖的调度任务,以隔离解释的影响.
主要成果:
- 参与者更多地依赖于DRL调度器的合理性,但态度信任没有显著差异.
- 信任通过感知AI能力间接增加,这种效应在更难的任务中较小,在领域专家中较大.
- 依赖并不是由任务难度或用户专业知识来调节的.
结论:
- 简短的理由主要是作为能力线索,增强AI采用,但不一定是用户理解.
- 为了实现校准的信任,解释应该补充有关AI不确定性和局限性的指导,以适应受众和任务.
- 未来的研究应该探索方法,以加深用户的理解超出感知能力线索.
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