Machine learning can be fair and accurate

As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages. To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system. The underlying theoretical assumption is that these adjustments make the system less accurate. A CMU team aims to dispel that assumption in a new study, recently published in Nature Machine Intelligence. Rayid Ghani, a professor in the School of Computer Science's Machine Learning Department (MLD) and the Heinz College of Information Systems and Public Policy; Kit Rodolfa, a research scientist in MLD; and Hemank Lamba, a post-doctoral researcher in SCS, tested that assumption in real-world applications and found the trade-off was negligible in practice across a range of policy domains.