Toward End-User Debugging of Machine-Learned Classifiers

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a generated “program” tailored specifically to the behaviors of that end user, telling the computer what to do when future inputs arrive. Researchers, however, have only recently begun to explore how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to test and debug learned programs so that everyone can benefit from intelligent programs adapted to their specific tasks and situations.