Improve Fairness with Shift-Adjusted Neyman–Pearson Classifiers via Single Index Modeling (SACSI) Presentation

Music City Center 

Neyman-Pearson classifiers, which aim to maximize the clinical benefit while adhering to risk constraints, are crucial in many practical fields, including early cancer detection. However, applying these classifiers can be challenging due to discrepancies between the data distributions of the source and target populations. The potential impact can be disproportionally severe on the under-represented groups. We propose a semi-parametric model-based approach for adapting NP classifier decision rules to different populations while equitably controlling classification errors specific to clinical applications. Our method involves a shift-adjustment strategy that leverages from the target population a small unlabeled sample and minimal auxiliary information alongside the labeled source data. This approach enhances the fairness of the learned decision rules and ensures they are consistently tailored for the target population. We demonstrate the performance through theoretical studies and simulations and illustrate the approach with an example of a prostate cancer study.

Algorithm fairness

Data shift

Neyman-Pearson Classifier