Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN. (arXiv:2211.08742v1 [cs.LG])

Auditing machine learning-based (ML) healthcare tools for bias is critical to
preventing patient harm, especially in communities that disproportionately face
health inequities. General frameworks are becoming increasingly available to
measure ML fairness gaps between groups. However, ML for health (ML4H) auditing
principles call for a contextual, patient-centered approach to model
assessment. Therefore, ML auditing tools must be (1) better aligned with ML4H
auditing principles and (2) able to illuminate and characterize communities
vulnerable to the most harm. To address this …

algorithmic fairness arxiv fairness health machine machine learning