🤖 AI Summary
This work addresses the challenge of utility loss under strict resource constraints in machine learning–based decision-making, where conventional fairness criteria can incur unbounded utility degradation and exacerbate inter-group resource disparities. To mitigate this, the authors propose an enhanced proportional fairness criterion alongside a novel variant of equality of opportunity, both of which guarantee bounded fairness-induced utility costs. Building upon algorithmic fairness theory and resource allocation models, they formulate a constrained optimization framework that systematically quantifies utility loss across different fairness definitions. Theoretical analysis and empirical evaluations demonstrate that the proposed approaches significantly improve robustness while preserving fairness, thereby achieving an effective trade-off between accuracy and fairness under resource limitations.
📝 Abstract
In recent years many important societal decisions are made by machine-learning algorithms, and many such important decisions have strict capacity limits, allowing resources to be allocated only to the highest utility individuals. For example, allocating physician appointments to the patients most likely to have some medical condition, or choosing which children will attend a special program. When performing such decisions, we consider both the prediction aspect of the decision and the resource allocation aspect. In this work we focus on the fairness of the decisions in such settings. The fairness aspect here is critical as the resources are limited, and allocating the resources to one individual leaves less resources for others. When the decision involves prediction together with the resource allocation, there is a risk that information gaps between different populations will lead to a very unbalanced allocation of resources.
We address settings by adapting definitions from resource allocation schemes, identifying connections between the algorithmic fairness definitions and resource allocation ones, and examining the trade-offs between fairness and utility. We analyze the price of enforcing the different fairness definitions compared to a strictly utility-based optimization of the predictor, and show that it can be unbounded. We introduce an adaptation of proportional fairness and show that it has a bounded price of fairness, indicating greater robustness, and propose a variant of equal opportunity that also has a bounded price of fairness.