测量算法偏差以分析AI工具的可靠性,这些工具使用智能手机感知行为数据预测抑郁风险
Daniel A Adler1, Caitlin A Stamatis2, Jonah Meyerhoff2
1Cornell Tech, Information Science, 2 W Loop Rd, New York, NY, 10044, USA. daa243@cornell.edu.
Npj mental health research
|April 22, 2024
概括
心理健康护理的AI工具在不同人群中显示精度降低,因为行为数据在各个子组之间不一致. 这凸显了针对特定的人口和社会经济群体量身定制的人工智能解决方案的需要.
科学领域:
- 数字心理健康数字心理健康
- 医疗保健中的人工智能
- 计算精神病学是一种计算精神病学.
背景情况:
- 人工智能工具旨在使用智能手机传感器数据远程估计抑郁风险.
- 当前的人工智能工具在同质人群中显示出高精度,但在多样化的群体中表现不佳.
研究的目的:
- 调查为什么人工智能抑郁风险预测工具在不同人群中不那么准确.
- 在子组中识别算法偏差和不一致的行为预测因素.
主要方法:
- 通过识别人工智能工具表现不佳的子组来评估算法偏差.
- 分析了感知行为作为人口统计和社会经济小组中抑郁症预测者的一致性.
主要成果:
- 检测到算法偏差,某些子组被错误地预测为较低的抑郁风险.
- 预测抑郁风险的感知行为在不同的人口和社会经济小组中被发现是不一致的.
结论:
- 感知行为是不同个体抑郁症的不可靠预测因素.
- 人工智能工具的通用性是一个关键问题;建议针对特定人群量身定制的解决方案.
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