Modern Foundations of Social Prediction - Juan Carlos Perdomo

Abstract: Machine learning excels at pattern recognition. Yet, when we deploy learning algorithms in social settings, we do not just aim to detect patterns; we use predictions to shape outcomes. This dynamic interplay, where we build systems using historical data to influence future behavior, underscores the role of prediction as both a lens and engine of social patterns. It also inspires us as researchers to explore new questions about which patterns we can influence, how to design prediction systems, and how to evaluate their impacts on society. Juan will begin by presenting insights from his work on performative prediction: a learning-theoretic framework that formalizes the dynamic aspects of social prediction. In the second half, he will present an empirical case study evaluating the impact of a risk prediction tool used to allocate interventions to hundreds of thousands of public school students each year. Juan will end with some discussion of future work and the challenges that lie ahead. Bio: Juan Carlos Perdomo is a postdoctoral fellow at the Harvard Center for Research on Computation and Society, advised by Cynthia Dwork. He earned his PhD from the University of California, Berkeley, where he was co-advised by Peter Bartlett and Moritz Hardt. Juan’s research focuses on the theoretical and empirical foundations of machine learning in society.