The Fairness-Performance Pareto Frontier: How to Design Less Discriminatory Algorithms?

Mieke Wilms, Christoph Heitz
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:196-200, 2026.

Abstract

In our recent publication we derive an optimality theorem for binary data-based decision systems which characterizes the Pareto-optimal systems in the performance-fairness space, for a wide range of group fairness scores and arbitrary performance utility functions[11]. The theorem gives a theoretical boundary for the Pareto frontier that cannot be exceeded by any algorithm, and proves that these solutions are deterministic group-specific threshold rules. We recapitulate the most important findings of this paper under the perspective of their legal implications. A decision system to be qualified as legally non-discriminatory needs to pass the so-called proportionality test which (among other questions) requires a proof that the goal of the decision maker could not have been achieved with less discriminatory means. Technically, this implies that the decision system is Pareto-optimal in the performance-fairness space, evaluated with a suitable fairness metric. We show that the optimality theorem of [11] helps to deal with the legal question of the proportionality test, but at the same time leads to puzzling questions with respect to the legal concepts of direct and indirect discrimination.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-wilms26a, title = {The Fairness-Performance Pareto Frontier: How to Design Less Discriminatory Algorithms?}, author = {Wilms, Mieke and Heitz, Christoph}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {196--200}, 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/wilms26a/wilms26a.pdf}, url = {https://proceedings.mlr.press/v350/wilms26a.html}, abstract = {In our recent publication we derive an optimality theorem for binary data-based decision systems which characterizes the Pareto-optimal systems in the performance-fairness space, for a wide range of group fairness scores and arbitrary performance utility functions[11]. The theorem gives a theoretical boundary for the Pareto frontier that cannot be exceeded by any algorithm, and proves that these solutions are deterministic group-specific threshold rules. We recapitulate the most important findings of this paper under the perspective of their legal implications. A decision system to be qualified as legally non-discriminatory needs to pass the so-called proportionality test which (among other questions) requires a proof that the goal of the decision maker could not have been achieved with less discriminatory means. Technically, this implies that the decision system is Pareto-optimal in the performance-fairness space, evaluated with a suitable fairness metric. We show that the optimality theorem of [11] helps to deal with the legal question of the proportionality test, but at the same time leads to puzzling questions with respect to the legal concepts of direct and indirect discrimination.} }
Endnote
%0 Conference Paper %T The Fairness-Performance Pareto Frontier: How to Design Less Discriminatory Algorithms? %A Mieke Wilms %A Christoph Heitz %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-wilms26a %I PMLR %P 196--200 %U https://proceedings.mlr.press/v350/wilms26a.html %V 350 %X In our recent publication we derive an optimality theorem for binary data-based decision systems which characterizes the Pareto-optimal systems in the performance-fairness space, for a wide range of group fairness scores and arbitrary performance utility functions[11]. The theorem gives a theoretical boundary for the Pareto frontier that cannot be exceeded by any algorithm, and proves that these solutions are deterministic group-specific threshold rules. We recapitulate the most important findings of this paper under the perspective of their legal implications. A decision system to be qualified as legally non-discriminatory needs to pass the so-called proportionality test which (among other questions) requires a proof that the goal of the decision maker could not have been achieved with less discriminatory means. Technically, this implies that the decision system is Pareto-optimal in the performance-fairness space, evaluated with a suitable fairness metric. We show that the optimality theorem of [11] helps to deal with the legal question of the proportionality test, but at the same time leads to puzzling questions with respect to the legal concepts of direct and indirect discrimination.
APA
Wilms, M. & Heitz, C.. (2026). The Fairness-Performance Pareto Frontier: How to Design Less Discriminatory Algorithms?. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:196-200 Available from https://proceedings.mlr.press/v350/wilms26a.html.

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