A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations, which spreads through collected data. When not properly accounted for, machine learning (ML) models learned from data can reinforce the structural biases already present in society. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches show regularly biased behaviors. However, we show that standard mitigation techniques, and our own post-hoc method, can be effective in reducing the level of unfair bias. We provide practical recommendations to develop ML models for depression risk prediction with increased fairness and trust in the real world. No single best ML model for depression prediction provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions.
翻译:在心理保健方面存在着严重的耻辱和不平等,特别是在服务不足的人口,这种耻辱和不平等是通过收集的数据传播的。在没有适当说明的情况下,从数据中汲取的机器学习模式可以强化社会已经存在的结构性偏见。在这里,我们在涉及不同国家和人口的4个不同案例研究中,对用于预测抑郁症的ML模式中的偏见进行系统研究。我们发现标准ML方法经常显示有偏见的行为。然而,我们表明,标准的缓解技术以及我们自己的控制后方法能够有效地减少不公平偏见的程度。我们提出切实可行的建议,以在现实世界中更加公平和更加信任的方式开发低压风险预测的ML模式。没有任何最佳的低压预测模式能够提供平等的结果。这强调了在模式选择中分析公平性以及透明地报告减少偏见的干预措施的影响的重要性。