Delayed Impact of Fair Machine Learning

TL;DR

This paper examines how static fairness criteria in machine learning can negatively impact long-term well-being of groups, revealing that fairness interventions may sometimes cause harm over time and emphasizing the importance of temporal considerations.

Contribution

It provides a comprehensive analysis of the delayed effects of standard fairness criteria, highlighting their potential to cause harm and the influence of measurement error on their performance.

Findings

Fairness criteria may hinder long-term improvement.

Different fairness criteria exhibit distinct long-term behaviors.

Measurement error can improve fairness criteria performance.

Abstract

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.