Happiness as a Measure of Fairness

Georg Pichler, Marco Romanelli, Pablo Piantanida
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:163-171, 2026.

Abstract

In this paper, we propose a novel fairness framework grounded in the concept of \emph{happiness}, a measure of the utility each group gains from decision outcomes. By capturing fairness through this intuitive lens, we not only offer a more human-centered approach, but also one that is mathematically rigorous: In order to compute the optimal, fair post-processing strategy, only a linear program needs to be solved. This makes our method both efficient and scalable with existing optimization tools. Furthermore, it unifies and extends several well-known fairness definitions, and our empirical results highlight its practical strengths across diverse scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-pichler26a, title = { Happiness as a Measure of Fairness }, author = {Pichler, Georg and Romanelli, Marco and Piantanida, Pablo}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {163--171}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/pichler26a/pichler26a.pdf}, url = {https://proceedings.mlr.press/v300/pichler26a.html}, abstract = { In this paper, we propose a novel fairness framework grounded in the concept of \emph{happiness}, a measure of the utility each group gains from decision outcomes. By capturing fairness through this intuitive lens, we not only offer a more human-centered approach, but also one that is mathematically rigorous: In order to compute the optimal, fair post-processing strategy, only a linear program needs to be solved. This makes our method both efficient and scalable with existing optimization tools. Furthermore, it unifies and extends several well-known fairness definitions, and our empirical results highlight its practical strengths across diverse scenarios. } }
Endnote
%0 Conference Paper %T Happiness as a Measure of Fairness %A Georg Pichler %A Marco Romanelli %A Pablo Piantanida %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-pichler26a %I PMLR %P 163--171 %U https://proceedings.mlr.press/v300/pichler26a.html %V 300 %X In this paper, we propose a novel fairness framework grounded in the concept of \emph{happiness}, a measure of the utility each group gains from decision outcomes. By capturing fairness through this intuitive lens, we not only offer a more human-centered approach, but also one that is mathematically rigorous: In order to compute the optimal, fair post-processing strategy, only a linear program needs to be solved. This makes our method both efficient and scalable with existing optimization tools. Furthermore, it unifies and extends several well-known fairness definitions, and our empirical results highlight its practical strengths across diverse scenarios.
APA
Pichler, G., Romanelli, M. & Piantanida, P.. (2026). Happiness as a Measure of Fairness . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:163-171 Available from https://proceedings.mlr.press/v300/pichler26a.html.

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