Abstract
Static classification has been the predominant focus
of the study of fairness in machine learning. While
most models do not consider how decisions change
populations over time, it is conventional wisdom
that fairness criteria promote the long-term wellbeing of groups they aim to protect. This work
studies the interaction of static fairness criteria with
temporal indicators of well-being. We show a simple one-step feedback model in which common criteria do not generally promote improvement over
time, and may in fact cause harm. Our results highlight the importance of temporal modeling in the
evaluation of fairness criteria, suggesting a range
of new challenges and trade-offs