SODAS Lecture with Mario L. Small

Title: The Data Revolution and the Study of Social Inequality: Promise and Perils

Abstract: The social sciences are in the midst of a revolution in access to data, as governments and private companies have accumulated vast digital records of rapidly multiplying aspects of our lives. The availability of such large-scale data has undergirded the spread of computational social science. How will the data revolution affect the study of social inequality? I argue that while the availability of large-scale data promises a dramatic transformation in the questions we can answer, this promise has been undercut by size-induced blindness, the tendency to ignore important limitations amidst a source with millions or billions of data points. Among the problems caused by such blindness, I identify selection, lamp-posting, algorithmic confounding, and unobserved influence. I present examples and discuss possible solutions.

Mario L. Small, Ph.D., is Quetelet Professor of Social Science at Columbia University. A University of Bremen Excellence Chair, and an elected member of the National Academy of Sciences, the American Academy of Arts and Sciences, the American Academy of Political and Social Sciences, and the Sociological Research Association, Small has published award-winning articles and books on urban inequality, personal networks, and the relationship between qualitative and quantitative methods. Small is currently studying the relationship between networks and decision-making, the ability of large-scale data to answer critical questions about urban inequality, and the relation between qualitative and quantitative methods.