There is growing concern that the potential of black box AI may exacerbate health-related disparities and biases such as gender and ethnicity in clinical decision-making. Biased decisions can arise from data availability and collection processes, as well as from the underlying confounding effects of the protected attributes themselves. This work proposes a machine learning-based orthogonal approach aiming to analyze and suppress the effect of the confounder through discriminant dimensionality reduction and orthogonalization of the protected attributes against the primary attribute information. By doing so, the impact of the protected attributes on disease diagnosis can be realized, undesirable feature correlations can be mitigated, and the model prediction performance can be enhanced.

翻译：随着黑盒人工智能在临床决策中可能加剧性别、种族等健康相关差异与偏见的担忧日益增长。有偏决策既可能源于数据可用性和收集过程，也可能源于保护属性本身潜在的混杂效应。本文提出一种基于机器学习的正交方法，旨在通过判别式降维以及将保护属性与主要属性信息进行正交化处理，对混杂因素进行分析与抑制。通过这一处理，可以揭示保护属性对疾病诊断的影响，减轻不良特征相关性，并提升模型预测性能。