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arXiv · 2309.13786

Distribution-Free Statistical Dispersion Control for Societal Applications

Zhun Deng·Thomas P. Zollo·Jake C. Snell·Toniann Pitassi·Richard Zemel
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Abstract

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 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.

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Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard Zemel. 2023-09-25. Distribution-Free Statistical Dispersion Control for Societal Applications. https://arxiv.org/abs/2309.13786

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