仔细观察经验,任务领域和自信如何影响对算法的依赖
Sarah A Jessup1, Gene M Alarcon2, Sasha M Willis3
1Consortium of Universities, Wright-Patterson AFB, OH, United States.
Applied ergonomics
|August 3, 2024
概括
算法依赖更受自我信心和任务领域的影响,而不是模型经验. 了解这些因素是有效预测和避免低于最佳表现的关键.
科学领域:
- 人与计算机的交互
- 决策科学 决策科学 决策科学
- 机器学习 机器学习
背景情况:
- 之前的研究表明,算法经验减少了依赖行为.
- 模型经验对依赖意图的影响仍然未被探索.
- 自信和领域知识被假设会影响算法依赖.
研究的目的:
- 调查统计模型经验,任务领域和自信对依赖意图和行为的影响.
- 检查个人估计和模型预测对感知准确性的影响.
- 了解驱动预测者在模型预测和自我估计之间做出决定的因素.
主要方法:
- 在线研究,347名参与者完成了预测任务.
- 统计分析以评估模型经验,任务领域和自我信心的影响.
- 测量依赖意图,依赖行为和感知的准确性.
主要成果:
- 自信和任务领域显著影响了依赖的意图,行为和感知的准确性.
- 模型经验没有显著影响依赖行为,意图或感知精度.
- 任务领域 (二手车销售,GPA,GitHub提取请求) 显示出显著的影响.
结论:
- 自信和任务领域是算法依赖的更强有力的预测因素,而不是模型经验.
- 个体差异和局势因素对预测决策具有重要影响.
- 过度依赖自我估计或模型预测可能会导致低于最佳的结果.
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