Making machine learning more useful to high-stakes decision makers

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.