  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.
