[2604.04858] FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models

Computer Science > Machine Learning arXiv:2604.04858 (cs) [Submitted on 6 Apr 2026] Title:FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models Authors:Nick Souligne, Vignesh Subbian View a PDF of the paper titled FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models, by Nick Souligne and 1 other authors View PDF HTML (experimental) Abstract: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: Obser...