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24th Annual Lecture
Gordon McKay Professor of Computer Science
John Paulson School of Engineering and Applied Sciences
Harvard Data Science Initiative
Friday, April 8, 2022
Fairness, Randomness, and the Crystal Ball
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, how can we even specify what we want from an ideal algorithm?
In this talk, we will introduce ‘outcome indistinguishability’ — a desideratum with roots in complexity theory. The talk will also situate the concept within the 10-year history of the theory of algorithmic fairness and the four-decade literature on forecasting.