Fairness, Randomness, and the Crystal Ball

  • Speaker
  • Cynthia Dwork, Ph.D.Gordon McKay Professor of Computer Science, John Paulson School of Engineering and Applied Sciences, Harvard University
    Radcliffe Alumnae Professor, Radcliffe Institute for Advanced Study
    Affiliated Faculty, Harvard Law School and Harvard Department of Statistics
Date & Time

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Prediction algorithms score individuals or individual instances, assigning to each one a number in the range from 0 to 1. That score is often interpreted as a probability: What are the chances that this loan will be repaid? How likely is this tumor to metastasize? What is the likelihood that this person will commit a violent crime in the next two years? A key question lingers: What is the probability of a non-repeatable event? Without a satisfactory answer, we cannot even specify the goal of an ideal algorithm.

In this talk, Cynthia Dwork will introduce ‘outcome indistinguishability’ — a desideratum with roots in complexity theory. She will situate the concept within the 10-year history of the theory of algorithmic fairness and the four-decade literature on forecasting.

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On-site registration will not be permitted. Walk-in entry will be denied.

About the Speaker

Dwork is a professor of computer science at Harvard University, affiliated faculty at Harvard Law School, and a distinguished scientist at Microsoft. Her work has established the pillars of fault-tolerant distributed systems, modernized cryptography to the ungoverned interactions of the internet and the era of quantum computing, revolutionized privacy-preserving statistical data analysis, and launched the theory of algorithmic fairness. She is the recipient of numerous awards and is a member of the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, and the American Philosophical Society.

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