Delayed Impact of Fair Machine Learning
This work addresses a critical gap in fairness research for machine learning practitioners and policymakers by revealing potential negative long-term impacts of standard fairness approaches, highlighting new challenges in temporal modeling.
The paper tackles the problem that static fairness criteria in machine learning may not promote long-term well-being and can cause harm over time, demonstrating that common criteria do not generally lead to improvement and may worsen outcomes in a one-step feedback model.
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even 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 in 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.