Abstract

Fairness in machine learning has predominantly been studied in static classification settings wit out concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the longterm well-being of those groups they aim to protect. We study how static fairness criteria intera with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that eve in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes i which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.