Title: Prediction, Fairness, and ... Complexity Theory?
Abstract: Prediction algorithms score individuals, assigning a number between zero and one that is often interpreted as an individual probability: a 0.7 “chance” that this child is in danger in the home; an 80% “probability” that this woman will succeed if hired; a 1/3 “likelihood” that they will graduate within 4 years of admission. But what do words like “chance,” “probability,” and “likelihood” mean for a non-repeatable activity like going to college? Absent an answer to this question, how can we even specify the goal, let alone evaluate the quality of, a prediction algorithm? Undaunted, machine-learned algorithms churn these numbers out in droves, sometimes with life-altering consequences.
An explosion of research in the theory of algorithmic fairness deploys insights from complexity theory to yield some tantalizing answers to these questions, together with a supporting algorithmic framework.
This talk will survey the confluence of fairness, complexity and prediction, tracing the history of key concepts and revealing some surprising contributions of fairness concepts to deep learning and complexity, even when fairness is not a concern.
Bio: Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard, and Affiliated Faculty at Harvard Law School and Department of Statistics, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. She has also made seminal contributions in cryptography and distributed computing, and she spearheaded the investigation of the theory of algorithmic fairness, her current focus. Dwork is the recipient of numerous awards including the IEEE Hamming Medal, the RSA award for Excellence in Mathematics, the Dijkstra, Gödel, and Knuth Prizes, and the ACM Paris Kanellakis Theory and Practice Award. She is a member of the US National Academy of Sciences and the US National Academy of Engineering, and is a Fellow of the American Academy of Arts and Sciences and the American Philosophical Society.