在人工智能驱动的推中审计认知漂移:一个负责任的人工智能方法协议与健康案例演示
1Universitat Zurich Wirtschaftswissenschaftliche Fakultat, Zürich, Switzerland.
Frontiers in neuroscience
|December 25, 2025
概括
我们开发了一个协议和认知漂移指数 (CDI) 来跟踪算法策划的变化. 这种可解释,敏感和可重复的方法有助于负责任的AI和认知研究.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 人工智能伦理学 人工智能伦理学
背景情况:
- 算法策划可以诱导认知漂移,影响用户的感知和行为.
- 现有的方法缺乏可解释性和可移植性来跟踪这些变化.
- 认知和神经认知研究需要强大的工具来评估算法影响.
研究的目的:
- 提出和验证一个用于检测和跟踪认知漂移的协议.
- 为量化这些变化引入认知漂移指数 (CDI).
- 将CDI分数映射到对负责任人工智能的可操作治理带.
主要方法:
- 开发一种用于认知漂移检测的协议.
- 构建和验证一个三维认知漂移指数 (CDI).
- 应用加权最小方形ANOVA用于路径效应估计,使用健康短视频作为案例研究.
主要成果:
- 该协议证明了可解释性,灵敏性,可复制性和跨领域的可移植性.
- CDI有效量化认知漂移,具有既定的范围和行动频段.
- 稳定性测试证实了协议的一致模式和实际实用性.
结论:
- 拟议的协议和CDI提供了一种可靠的方法来监测算法对认知的影响.
- 该工具支持从业人员和负责的人工智能团队实施适当的治理行动.
- 该框架可适应多模式验证,包括眼睛跟踪和EEG研究.
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