Designing Algorithmic Ensembles for Fair and Accurate AI-based Mammography Screening

As AI algorithms become increasingly prevalent in healthcare, ensuring both accuracy and fairness in decision-making poses a dual challenge. Breast cancer screening is a particularly high-stakes example: FDA-cleared AI tools are already in clinical use, and nearly 40 million mammograms are performed annually in the United States, yet many of these systems have been trained on limited subpopulations, raising equity concerns. We address this challenge by leveraging the diversity of predictive algorithms developed for the same task and forming a linear ensemble. We develop a statistical model that establishes conditions under which such an ensemble can satisfy equal-opportunity fairness and then demonstrate its application using simulated data calibrated from a real-world AI mammography competition. Our analysis shows that ensembles can improve both accuracy and fairness, especially when constituent algorithms differ in subgroup performance. These findings provide actionable guidance for health.