2023/09/25 by Zhun Deng, Thomas P. Zollo, Deng, Zhun +7 · 1 citation
Social Sciences · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Misinformation and Its Impacts
paper · pdf · doi:10.48550/arxiv.2309.13786
openalex publication_date 2023/09/25 · openalex created_date 2023/09/27 · openalex updated_date 2026/07/28
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.