Distribution-Free Statistical Dispersion Control for Societal Applications
Main Conference Track
Authors: Zhun Deng, Thomas Zollo, Jake Snell, Toniann Pitassi, Richard Zemel

Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning.  Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range.  However, for many high-stakes applications it is crucial to understand and control the \textit{dispersion} of a loss distribution, or the extent to which different members of a population experience unequal effects of algorithmic decisions. We initiate the study of distribution-free control of statistical dispersion measures with societal implications and propose a simple yet flexible framework that allows us to handle a much richer class of statistical functionals beyond previous work. Our methods are verified through experiments in toxic comment detection, medical imaging, and film recommendation.