工作负载和任务优先级对多任务性能的影响和依赖1级可解释AI (XAI) 使用
Jawad Alami1, Mohamad El Iskandarani1, Sara Lu Riggs1
1University of Virginia, USA.
Human factors
|March 12, 2025
概括
关键任务中的高工作负载增加了对AI警报的依赖,但减少了警报验证. 任务优先级也会影响人工智能解释的使用,这对于在高风险环境中校准人工智能信任至关重要.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 人工智能的人工智能
背景情况:
- 在关键环境中的运营商面临着影响业绩的多任务挑战.
- 可解释的人工智能 (XAI) 可以支持决策,但其在多任务处理中的应用尚未得到充分理解.
- 一级XAI为援助运营商提供基本的感知信息.
研究的目的:
- 检查工作负载和任务优先级如何影响多任务性能.
- 在高风险场景中调查运营商对1级XAI系统的依赖.
- 了解工作负载,任务优先级和XAI利用之间的相互作用.
主要方法:
- 一个在实验对象内部的实验,与30名参与者进行模拟无人机指挥和控制任务.
- 操纵工作量 (低,中,高) 和人工智能辅助任务优先级 (低,高).
- 测量性能指标,包括准确性,人工智能依赖性和警报检测.
主要成果:
- 增加的工作量降低了人工智能辅助任务的性能,并增加了对人工智能系统的依赖,特别是在任务优先级较低的情况下.
- 任务优先级显著影响了AI解释的使用.
- 运营商在工作量高的情况下对人工智能警报的依赖增加,但警报验证减少.
结论:
- 工作负载影响运营商对人工智能的依赖,需要仔细校准人工智能对关键系统的信任.
- 任务优先级是操作员如何处理AI解释的一个关键因素.
- 结果为高风险环境的AI系统的设计提供了信息,以确保适当的AI依赖.
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