Outcome Indistinguishability and its Diverse Applications

Abstract

Outcome Indistinguishability, a notion from algorithmic fairness with roots in complexity theory, frames learning not as loss minimization – the dominant paradigm in supervised machine learning -- but instead as satisfaction of a collection of “indistinguishability” constraints. Outcome Indistinguishability considers two alternate worlds on individual-outcome pairs: in the natural world, individuals’ outcomes are generated by Real-Life’s true distribution; in the simulated world, individuals’ outcomes are sampled according to a predictive model. Outcome Indistinguishability requires the learner to produce a predictor in which the two worlds are computationally indistinguishable.  The notion has provided a generous springboard, first and foremost in machine learning, and also in complexity theory.

Bio

Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard and Affiliated Faculty at Harvard Law School and the 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 National Medal of Science, 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.