Distribution-Free Statistical Dispersion Control for Societal Applications

• URL: http://arxiv.org/abs/2309.13786v2
• Date: Wed, 6 Mar 2024 14:18:52 GMT
• Title: Distribution-Free Statistical Dispersion Control for Societal Applications
• Authors: Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard Zemel
• Abstract summary: 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. We propose a simple yet flexible framework that allows us to handle a much richer class of statistical functionals beyond previous work.
• Score: 16.43522470711466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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