Fairness under Competition
This work highlights a critical issue for policymakers and practitioners in ML, revealing that standard fairness adjustments may be insufficient or harmful in competitive settings, making it a novel and essential contribution.
The paper tackles the problem of algorithmic fairness in competitive environments, showing that individually fair classifiers can lead to unfair ecosystem outcomes, with theoretical and experimental evidence quantifying fairness loss based on classifier correlation and data overlap.
Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal Opportunity. In this paper we consider the effects of adopting such fair classifiers on the overall level of ecosystem fairness. Specifically, we introduce the study of fairness with competing firms, and demonstrate the failure of fair classifiers in yielding fair ecosystems. Our results quantify the loss of fairness in systems, under a variety of conditions, based on classifiers' correlation and the level of their data overlap. We show that even if competing classifiers are individually fair, the ecosystem's outcome may be unfair; and that adjusting biased algorithms to improve their individual fairness may lead to an overall decline in ecosystem fairness. In addition to these theoretical results, we also provide supporting experimental evidence. Together, our model and results provide a novel and essential call for action.