本质干预对自动化系统解释性和用户决策的影响
Lydia P Gleaves1, David A Broniatowski2
1Department of Engineering Management and Systems Engineering, The George Washington University, 800 22nd St. NW, Washington, DC, 20052, USA.
Cognitive research: principles and implications
|October 8, 2024
概括
了解自动化系统是关键. 提供系统输出的"核心"或底线含义,显著提高了用户检测协调错误信息活动的能力,提高了决策能力.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 信息科学 信息科学 信息科学
背景情况:
- 自动化系统越来越普遍,但往往是不透明的.
- 用户解释自动化系统输出的能力是一个越来越大的挑战.
- 模糊痕迹理论提供了对定量信息解释的见解.
研究的目的:
- 用在线决策辅助器测试模糊追踪理论对用户决策的预测.
- 检查用户对系统可解释性的认可与检测协调错误信息的性能之间的关系.
- 评估旨在改善系统输出感知的干预措施.
主要方法:
- 招募了205名在线群众工作者使用基于URL的错误信息检测系统.
- 评估了用户对系统可解释性的认可,并与任务性能相关联.
- 实施了"核心"干预,而不是系统输出的文字定量指标.
主要成果:
- 支持系统可解释性的用户在检测错误信息方面表现出更强的辨别能力.
- 可解释性与客观的数学能力和自信相关.
- "核心"干预组在识别错误信息URL方面表现优于字面指标组.
结论:
- 使用户能够掌握自动化系统的基本"核心"意义,提高了性能.
- 对自动化系统输出的理解对于有效的决策至关重要.
- 促进基本理解的干预措施,无论个人差异如何,都有利于用户.
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