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.

翻译：显式的有限样本统计保证是负责任机器学习的重要组成。以往工作主要聚焦于约束预测器的期望损失，或单个预测的损失值落在指定范围内的概率。然而，对于许多高风险应用而言，理解并控制损失分布的离散度至关重要——即不同群体成员所受算法决策影响的差异程度。我们率先开展具有社会影响的统计离散度免分布控制研究，提出一个简洁而灵活的框架，可处理比以往工作更丰富的统计泛函类别。通过毒性评论检测、医学影像和电影推荐等实验验证了本方法的有效性。