Resource-constrained Fairness
This addresses the gap in fair ML tools for real-world deployment by incorporating resource limitations, though it is incremental in extending existing fairness frameworks.
The paper tackles the problem of ensuring fairness in machine learning systems under real-world resource constraints, demonstrating that the cost of fairness varies significantly with the level of available resources, which was previously overlooked.
Access to resources strongly constrains the decisions we make. While we might wish to offer every student a scholarship, or schedule every patient for follow-up meetings with a specialist, limited resources mean that this is not possible. When deploying machine learning systems, these resource constraints are simply enforced by varying the threshold of a classifier. However, these finite resource limitations are disregarded by most existing tools for fair machine learning, which do not allow the specification of resource limitations and do not remain fair when varying thresholds. This makes them ill-suited for real-world deployment. Our research introduces the concept of "resource-constrained fairness" and quantifies the cost of fairness within this framework. We demonstrate that the level of available resources significantly influences this cost, a factor overlooked in previous evaluations.