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Nir Rosenfeld - Classification Under Strategic Self-Selection

Presented on Thursday, July 11th, 2024, 10:30 AM, room B220 Speaker Nir Rosenfeld (Technion) Title Classification Under Strategic Self-Selection Abstract: The growing success of machine learning across a wide range of domains and applications has made it appealing to use it also as a tool for informing decisions about humans, and in which human users are the target of prediction. But humans are not your conventional input: they have goals, beliefs, and aspirations, and often take action to promote their own self-interests. One such action is participation, namely users' decisions of whether to at all take part in the process – be it job applications, university admissions, loan requests, or welfare programs. Since who participates in these is likely to depend on the learned decision rule, learning becomes susceptible to self-selection – a common though easily overlooked source of bias that can significantly affect learning outcomes. Focusing on resume screening as an example task, I will present a learning setting in which the choice of classifier has influence over which candidates apply, and which do not. From a learning perspective, this becomes a problem of model-induced distribution shift, where the challenge is that each classifier ‘turns on’ or ‘turns off’ different parts of the data distribution, and for which we propose a differential optimization framework. From a policy perspective, we show that while conventional learning can lead to arbitrary outcomes, strategic learning (which anticipates user behavior) has the capacity to almost fully determine the composition of the applying sub-population. This has concrete implications on social outcomes which require us to rethink the meaning of equity, the role of affirmative action, and the need for regulation in learning. 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.