Department of Economics
October 19, 2021
Tags Homepage

Social Equity and Applied Mathematics Seminar 11/10-Cynthia Dwork


The Math CoOp at the Division of Applied Math at Brown University is running a new monthly seminar series on "Social Equity and Applied Mathematics" (SEAM).  Its aim is to discuss mathematical models, and theoretical and computational aspects of problems that are of relevance to social equity in the world. 

This seminar series aims to create broader awareness of varied societal issues and quantitative approaches used to study them, and bring together a diverse group of interdisciplinary researchers with different backgrounds and levels of experience who are deeply interested in these issues.  We hope it also inspires new directions of both applied research as well as fundamental mathematical theory that can shed light on solutions to these problems. All members of the Brown Community are welcome. 

The Fall series will be virtual -- the first lecture will be by:

Cynthia Dwork (Harvard University)

Time: November 10, 2021 (Wednesday), 12:00pm - 1:00pm/1:30pm (including additional Q&A)

Zoom Link

Title: What are YOUR chances? The defining problem of AI

Abstract: Prediction algorithms score individuals, or individual instances, assigning to each one a number in [0,1] that is often interpreted as a probability: What are the chances that this loan will be repaid?  How likely is the tumor to metastasize? What is the probability this person will commit a violent crime in the next two years?  But what is the probability of a non-repeatable event?  Without a satisfactory answer, we cannot even specify the goal of the ideal algorithm, let alone try to build it.This talk will introduce Outcome Indistinguishability, a desideratum with roots in complexity theory, and situate it in the context of research on the theory of algorithmic fairness.

Cynthia Dwork, Gordon McKay Professor of Computer Science at the Harvard Paulson School of Engineering, Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study, and Affiliated Faculty at Harvard Law School and Department of Statistics, uses theoretical computer science to place societal problems on a firm mathematical foundation.

She was awarded the Edsger W. Dijkstra Prize in 2007 in recognition of some of her earliest work establishing the pillars on which every fault tolerant system has been built for a generation (Dwork, Lynch, and Stockmeyer, 1984). 

Her contributions to cryptography include the launching of non-malleable cryptography, the subfield of modern cryptography that studies -- and remedies -- the failures of cryptographic protocols to compose securely (Dolev, Dwork, and Naor, 1991).  She is a co-inventor of the first public-key cryptosystem based on lattices, the current best bet for cryptographic constructions that will remain secure even against quantum computers (Ajtai and Dwork, 1997). More recently, Dwork spearheaded a successful effort to place privacy-preserving analysis of data on a firm mathematical foundation.  A cornerstone of this effort is the invention of Differential Privacy (Dwork, McSherry, Nissim, and Smith, 2006, Dwork 2006), now the subject of intense activity across many disciplines and recipient of the Theory of Cryptography Conference 2016 Test-of-Time award and the 2016 Gödel Prize. Now widely used in industry – for example by Google, MIcrosoft, Uber, and, most prominently, by Apple – differential privacy will also be the foundation of the Disclosure Avoidance System in the 2020 US Decennial Census.

Differentially private analyses enjoy a strong form of stability.  One consequence is statistical validity under adaptive (aka exploratory) data analysis, which is of great value even when privacy is not itself a concern (Dwork, Feldman, Hardt, Pitassi, Reingold, and Roth STOC 2015, and Science Magazine, 2015).

Data, algorithms, and systems have biases embedded within them reflecting designers' explicit and implicit choices, historical biases, and societal priorities. They form, literally and inexorably, a codification of values.  Unfairness of algorithms -- for tasks ranging from advertising to recidivism prediction -- has recently attracted considerable attention in the popular press.  Anticipating these concerns, Dwork initiated a formal study of fairness in classification (Dwork, Hardt, Pitassi, Reingold, and Zemel, 2012).  This is now a thriving subfield of theoretical computer science.

Dwork is currently working in all of these last three areas (differential privacy, statistical validity in adaptive data analysis, and the theory of algorithmic fairness).  Her current principal focus is a complexity-theoretic investigation of the meaning of "individual probabilities." See the Opportunities tab for more information.

Dwork was educated at Princeton and Cornell.  She received her BSE (with honors) in electrical engineering and computer science at Princeton University, where she also received the Charles Ira Young Award for Excellence in Independent Research, the first woman ever to do so.  She received her M.Sc. and Ph.D. degrees in computer science at Cornell University.

Dwork is a member of the US National Academy of Sciences and the US National Academy of Engineering, and is a fellow of the ACM, the American Academy of Arts and Sciences, and the American Philosophical Society.

For more information on the seminar series, see