逆转问题:为什么算法应该推断心理状态,而不仅仅是预测行为?
Jon Kleinberg1, Jens Ludwig2, Sendhil Mullainathan3
1Department of Computer Science, Cornell University.
概括
机器学习算法经常预测可观测的行为,但其目的是推断未被观察到的心理状态,创造"反转问题". 需要整合行为和计算科学的新工具来弥合这个差距,以便准确地建模人类行为.
科学领域:
- 计算社会科学 计算社会科学
- 行为科学 行为科学
- 机器学习伦理学 机器学习伦理学
背景情况:
- 机器学习越来越多地用于预测人类行为.
- 算法通常预测可观察到的行为 (例如点击,诊断),但旨在推断潜在的心理状态 (例如偏好,判断).
- 这种差异在算法设计和应用中造成了一个重要的,经常被忽视的问题.
研究的目的:
- 为了识别和定义的目标.
主要方法:
- 基于心理学和行为科学的概念分析.
- 检查推系统和医学诊断中的案例研究.
- 论证新的跨学科工具的必要性.
主要成果:
- 确定了一个名为"倒置问题"的无处不在的问题,其中算法预测了预期的心理状态的代理,而不是直接测量所需的心理状态.
- 心理学洞察力揭示了为什么行为数据可以成为真正目标的糟糕代理 (例如,无意识的点击,专家疲劳).
- 目前的机器学习方法不足以解决这些反转问题.
结论:
- 解决反转问题需要超越直接的行为预测.
- 整合行为和计算科学的新方法对于准确建模人类精神状态至关重要.
- 解决这些问题对于负责任的开发和部署机器学习在以人为中心的应用程序中至关重要.
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