A data-centric perspective to fair machine learning for healthcare

Abstract

Machine learning models are increasingly being deployed in real-world clinical settings and have shown promise in patient diagnosis, treatment and outcome tasks. However, such models have also been shown to exhibit biases towards specific demographic groups, leading to inequitable outcomes for under-represented or historically marginalized communities.