Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy

Kit T. Rodolfa, Hemank Lamba, Rayid Ghani

Abstract: A growing number of researchers are developing approaches to improve fairness in machine learning applications in areas such as healthcare, employment and social services, to avoid propagating and amplifying racial and other inequities. An empirical study explores the trade-off between increasing fairness and model accuracy across several social policy areas and finds that this trade-off is negligible in practice.