机器学习中的公平性和偏见校正用于四个研究群体的抑郁症预测
Vien Ngoc Dang1, Anna Cascarano2, Rosa H Mulder3,4
1Departament de Matemàtiques i Informàtica, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain. dangn@ub.edu.
Scientific reports
|April 3, 2024
概括
用于抑郁症预测的机器学习 (ML) 模型可以使精神卫生保健中的不平等持续存在. 缓解技术可以减少偏见,但谨慎的模型选择和透明的报告对于公平性至关重要.
科学领域:
- 心理健康研究 心理健康研究
- 医疗保健中的人工智能
- 计算社会科学 计算社会科学
背景情况:
- 精神卫生保健面临着严重的耻辱和不平等,特别是在服务不足的人群中.
- 现有的数据不平等可以被机器学习 (ML) 模型放大,加强结构偏差.
- 偏差的ML模型在临床应用中构成风险,例如抑郁症预测.
研究的目的:
- 系统地调查ML模型中的偏见,用于跨不同人口和国家的抑郁症预测.
- 评估标准和新型缓解技术在减少不公平偏见方面的有效性.
- 突出公平性分析和ML中的透明报告对心理健康的重要性.
主要方法:
- 使用四个不同的案例研究对ML模型进行了系统研究,用于预测抑郁症.
- 应用了标准的ML方法并评估了它们固有的偏差.
- 实施和评估既有和新的后期偏差缓解技术.
主要成果:
- 标准的ML方法在抑郁症预测中始终表现出偏见的行为.
- 偏见缓解技术,包括拟议的临时后方法,有效地减少了不公平的偏见.
- 没有一个单一的ML模型能够在所有人群中实现相同的结果,这凸显了公平的复杂性.
结论:
- 在选择ML模型来预测抑郁症时,公平必须是关键考虑因素.
- 透明地报告脱皮干预措施的影响至关重要.
- 实践者应该采用积极的习惯,并解决公开的挑战,以提高心理健康ML模型的公平性.
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