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cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions
Shashank Yadav, Vignesh Subbian
Interpretability plays a vital role in aligning and deploying deep learning models in critical care, especially in constantly evolving conditions that influence patient survival. H…