Monday April 28 4:00 - 4:50 PM Kelley 1001
Weng-Keen Wong Assistant Professor School of Electrical Engineering & Computer Science Oregon State University End-user debugging of machine learning systems How do you debug a program that was written by a machine instead of a person, especially when you do not know much about programming or machine learning, and are working with a program you cannot even see? This is the problem faced by users of a new type of program being used today, namely, machine learning systems that, after being deployed, customize themselves by learning from an end-user's behavior. Prime examples of these programs include adaptive user interfaces, intelligent desktop assistants, email classifiers, and recommender systems. Inaccurate predictions by these learning systems erode users' trust and curb widespread acceptance of such systems. We seek to improve both the performance and acceptance of machine learning systems by allowing end-users to debug these learned programs when they make incorrect predictions. Achieving this goal requires two key components: (1) providing explanations of the system's reasoning that is suitable for end-users untrained in machine learning and (2) improving the performance of the learned program by incorporating corrective feedback from the end-user. This talk will present our work on these two components along with results from a user study in which participants were allowed to provide corrective feedback to an email classifier. Biography Weng-Keen Wong is an Assistant Professor of Computer Science at Oregon State University. Prior to being a faculty member at OSU, he was a post-doctoral associate at the Center for Biomedical Informatics at the University of Pittsburgh. He received his Ph.D. (2004) and M.S. (2001) in Computer Science from Carnegie Mellon University, and his B.Sc. (1997) from the University of British Columbia. His Ph.D. thesis was entitled "Data Mining Algorithms for the Early Detection of Disease Outbreaks". Currently, his research areas are in data mining and machine learning, with specific interests in anomaly detection, human-in-the-loop learning, and applications of machine learning to ecological problems.
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