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Speaker: Nir Rosenfeld (Technion)
Title: Strategic Classification: Learning With Data That 'Behaves'
Abstract: The growing success of machine learning across a wide range of domains and applications has made it appealing to be used also as a tool for informing decisions about humans. But humans are not your conventional input: they have goals, beliefs, and aspirations, and take action to promote their own self-interests. Given that standard learning methods are not designed to handle inputs that "behave", it is natural to ask: how should we design learning systems when we know they will be deployed and used in social environments?
As a starting point, I will present the problem of strategic classification, in which users can modify their features (at a cost) in response to a learned classifier in order to obtain favorable predictions. I will then describe some of our work in this field, demonstrating how even mild forms of strategic behavior can dramatically transform the learning problem, and the role game theory can play in addressing some of the new challenges that arise. Finally, I will argue for strategic classification as a framework that can be useful for formally reasoning about learning under user behavior in general, and which holds potential for weaving more elaborate forms of economic modeling into the learning pipeline.
Bio: Nir Rosenfeld is an assistant professor of Computer Science at the Technion, where he is head of the Behavioral Machine Learning lab, working on problems at the intersection of machine learning and human behavior. Before joining the Technion he was a postdoc at Harvard's School of Engineering and Applied Sciences (SEAS), where he was a member of the EconCS group, a fellow of the Center for Research on Computation and Society (CRCS), and a fellow of the Harvard Data Science Initiative (HDSI). He holds a BSc in Computer Science and Psychology and an MSc and PhD in Computer Science, all from the Hebrew University.