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

2026-04-07

AI Summary

This study addresses the critical gap in clinical machine learning fairness evaluation by systematically applying an intersectional fairness auditing framework to real-world clinical prediction tasks. Leveraging the All of Us dataset, the authors integrate the FairLogue toolkit, observational fairness metrics, and counterfactual causal analysis to assess model performance across intersecting subgroups defined by race and gender. Their findings reveal substantial performance disparities that remain undetected under conventional single-axis fairness assessments. However, counterfactual experiments demonstrate that most of these disparities persist even after randomizing group identity, indicating that they primarily stem from differences in covariate distributions rather than direct discrimination. These results underscore the necessity and value of intersectional auditing for accurately diagnosing and addressing health inequities in clinical AI systems.

Abstract

Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined demographic groups. Using the All of Us dataset, two published models were selected for replication and evaluation: (A) prediction of selective serotonin reuptake inhibitor associated bleeding events and (B) two-year stroke risk in patients with atrial fibrillation. Observational fairness metrics were computed across race, gender, and intersectional subgroups, followed by counterfactual analysis to evaluate whether disparities were attributable to group membership. Intersectional evaluation revealed larger disparities than single-axis analyses; however, counterfactual diagnostics indicated that most observed disparities were comparable to those expected under randomized group membership. These results highlight the importance of intersectional fairness auditing and demonstrate how FairLogue provides deeper insight into bias in clinical machine learning systems.

Problem

• intersectional fairness
• clinical machine learning
• healthcare disparities
• bias auditing
• demographic bias

Innovation

• intersectional fairness
• FairLogue
• counterfactual analysis
• clinical machine learning
• healthcare bias