Fairness in limited resource prediction-driven decisions

In recent years, many high-stakes societal decisions are made by machine learning systems, often under strict capacity constraints that limit resources to a small subset of individuals. In such settings, information gaps across populations can lead to highly unbalanced allocations. We study prediction-driven resource allocation through the lens of fairness. We connect classical algorithmic fairness notions with resource allocation definitions, and characterize the trade-offs between fairness and utility. We introduce an adaptation of proportional fairness to this setting, showing that it yields a continuum of fairness criteria via a regularization parameter, along with quantitative bounds on the resulting price of fairness. We further show that max-min fairness and equal opportunity can incur an unbounded price of fairness in extreme cases, and propose a variant of equal opportunity with a bounded price of fairness.