工具包FairLogue可检测医疗模型中多重身份群体的公平性偏差。
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
- 扩展公平性指标至交叉身份群体，支持观测与反事实分析
- 发现交叉群体差异显著，如公平性差距达0.20，真阳性率差为0.33
- 适合医疗AI开发者和伦理审查者用于识别深层不公平现象
算法公平性对医疗机器学习的公平可信至关重要。现有工具多聚焦单一维度比较，可能忽略交叉身份群体的复合不公。本文提出FairLogue，一个基于Python的工具包，包含三部分：1）观测框架，将人口均等、平等机会等指标扩展至交叉身份群体；2）基于治疗情境的反事实框架；3）针对交叉身份干预的广义反事实框架。在All of Us Controlled Tier V8数据集的青光眼手术预测任务中评估，使用逻辑回归，以种族和性别为受保护属性。观测分析显示模型性能中等（AUROC=0.709；准确率=0.651），但存在显著交叉公平性差距：人口均等差异达0.20，平等机会的真阳性率与假阳性率差距分别为0.33和0.15。反事实分析采用置换零分布，获得的不公平度量（u-value）接近零，表明在控制协变量后，观察到的不公可能源于随机性。FairLogue提供模块化方案，整合观测与反事实方法，实现临床机器学习中交叉偏差的量化评估。
原文摘要 · Abstract (English)
Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting intersectional populations. This study introduces Fairlogue, a toolkit designed to operationalize intersectional fairness assessment in observational and counterfactual contexts within clinical settings. Methods: Fairlogue is a Python-based toolkit composed of three components: 1) an observational framework extending demographic parity, equalized odds, and equal opportunity difference to intersectional populations; 2) a counterfactual framework evaluating fairness under treatment-based contexts; and 3) a generalized counterfactual framework assessing fairness under interventions on intersectional group membership. The toolkit was evaluated using electronic health record data from the All of Us Controlled Tier V8 dataset in a glaucoma surgery prediction task using logistic regression with race and gender as protected attributes. Results: Observational analysis identified substantial intersectional disparities despite moderate model performance (AUROC = 0.709; accuracy = 0.651). Intersectional evaluation revealed larger fairness gaps than single-axis analyses, including demographic parity differences of 0.20 and equalized odds true positive and false positive rate gaps of 0.33 and 0.15, respectively. Counterfactual analysis using permutation-based null distributions produced unfairness ("u-value") estimates near zero, suggesting observed disparities were consistent with chance after conditioning on covariates. Conclusion: Fairlogue provides a modular toolkit integrating observational and counterfactual methods for quantifying and evaluating intersectional bias in clinical machine learning workflows.
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