Resource-constrained Fairness

Sofie Goethals, Eoin D. Delaney, Brent Mittelstadt, Chris Russell
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:435-452, 2026.

Abstract

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 make this infeasible. When deploying machine learning systems, these resource constraints are typically enforced by adjusting the classifier threshold. However, these finite resource limitations are disregarded by most existing tools for fair machine learning, which do not allow for 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 these constraints. We demonstrate that the level of available resources significantly influences this cost, a factor overlooked in prior evaluations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-goethals26b, title = {Resource-constrained Fairness}, author = {Goethals, Sofie and Delaney, Eoin D. and Mittelstadt, Brent and Russell, Chris}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {435--452}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/goethals26b/goethals26b.pdf}, url = {https://proceedings.mlr.press/v350/goethals26b.html}, abstract = {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 make this infeasible. When deploying machine learning systems, these resource constraints are typically enforced by adjusting the classifier threshold. However, these finite resource limitations are disregarded by most existing tools for fair machine learning, which do not allow for 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 these constraints. We demonstrate that the level of available resources significantly influences this cost, a factor overlooked in prior evaluations.} }
Endnote
%0 Conference Paper %T Resource-constrained Fairness %A Sofie Goethals %A Eoin D. Delaney %A Brent Mittelstadt %A Chris Russell %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-goethals26b %I PMLR %P 435--452 %U https://proceedings.mlr.press/v350/goethals26b.html %V 350 %X 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 make this infeasible. When deploying machine learning systems, these resource constraints are typically enforced by adjusting the classifier threshold. However, these finite resource limitations are disregarded by most existing tools for fair machine learning, which do not allow for 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 these constraints. We demonstrate that the level of available resources significantly influences this cost, a factor overlooked in prior evaluations.
APA
Goethals, S., Delaney, E.D., Mittelstadt, B. & Russell, C.. (2026). Resource-constrained Fairness. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:435-452 Available from https://proceedings.mlr.press/v350/goethals26b.html.

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