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
Simons Foundation Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite anyone interested in the topic to join us for this weekly lecture series.
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.
To attend this in-person event, you will need to register in advance and provide:
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On-site registration will not be permitted. Walk-in entry will be denied.