Taking machine thinking out of the black box


Software applications provide people with many kinds of automated decisions, such as identifying what an individual's credit risk is, informing a recruiter of which job candidate to hire, or determining whether someone is a threat to the public. In recent years, news headlines have warned of a future in which machines operate in the background of society, deciding the course of human lives while using untrustworthy logic. Part of this fear is derived from the obscure way in which many machine learning models operate. Known as black-box models, they are defined as systems in which the journey from input to output is next to impossible for even their developers to comprehend. "As machine learning becomes ubiquitous and is used for applications with more serious consequences, there's a need for people to understand how it's making predictions so they'll trust it when it's doing more than serving up an advertisement," says Jonathan Su, a member of the technical staff in MIT Lincoln Laboratory's Informatics and Decision Support Group.