4 papers
Advancing Health Equity through Multi-Level Fairness in Health Informatics
Nick Souligne, Vignesh Subbian
The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity. Specifically, the use of mult…
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
Nick Souligne, Isabella Mixton-Garcia, Vignesh Subbian
Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demogra…
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
Nick Souligne, Vignesh Subbian
Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independe…
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
Nick Souligne, Vignesh Subbian
Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may…