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.

在负责任的机器学习中，显式地对模型性能进行有限样本统计保证是重要的要素。我们引入了一个新的研究领域——具有社会影响的分布自由统计离散度控制，并提出了一个简单而灵活的框架，能够处理比以前更丰富的统计功能类别。我们通过毒性评论检测、医学图像和电影推荐的实验来验证我们的方法。

面向社会应用的无分布统计离散度控制