When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical, and normative arguments. At the center of our proposal is a family of simple and efficient statistical tests, called backward baselines, that demonstrate if, and to what extent, a model recounts the past. Our statistical theory provides guidance for interpreting backward baselines, establishing equivalences between different baselines and familiar statistical concepts. Concretely, we derive a meaningful backward baseline for auditing a prediction system as a black box, given only background variables and the system's predictions. Empirically, we evaluate the framework on different prediction tasks derived from longitudinal panel surveys, demonstrating the ease and effectiveness of incorporating backward baselines into the practice of machine learning.

翻译：机器学习模型何时能够预测个体的未来，又何时只是在复述先于个体存在的模式？在本文中，我们基于理论、实证和规范性论证，提出这两种预测路径之间的区分。我们方案的核心是一类简单高效的统计检验——称为反向基线——它能证明模型在多大程度上重述了过去。我们的统计理论为解释反向基线提供了指导，建立了不同基线方法与常见统计概念之间的等价关系。具体而言，我们推导出一种有意义的反向基线，用于将预测系统作为黑箱进行审计，仅需背景变量和系统的预测结果。在实证方面，我们基于纵向面板调查衍生的不同预测任务评估了该框架，展示了将反向基线融入机器学习实践的简便性和有效性。