FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program

ArXi:2604.16450v1 Announce Type: cross Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess graphic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined graphic groups.