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“Unfairness” of algorithms – for tasks ranging from advertising to recidivism prediction – has attracted considerable attention in the popular press. Algorithmic techniques for achieving fairness now routinely appear in dedicated workshops and symposia as well as in established research conferences. This talk will focus on the (relatively) new study of a mathematically rigorous theory of fairness: definitions, methods, and provable limits and tradeoffs, providing a lens for hot-button policy issues such as “interpretability” and raising new questions for future research. Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard and Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis, recognized by the 2016 Theory of Cryptography Conference Test-of-Time award and the Goedel Prize. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. Her most recent foci include stability in adaptive data analysis (especially via differential privacy) and fairness in classification. Dwork is a member of the US National Academy of Sciences and the US National Academy of Engineering, and the American Philosophical Society, and is a Fellow of the American Academy of Arts and Sciences.