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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01069 |
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| _version_ | 1866918183372324864 |
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| author | Pichler, Georg Romanelli, Marco Piantanida, Pablo |
| author_facet | Pichler, Georg Romanelli, Marco Piantanida, Pablo |
| contents | In this paper, we propose a novel fairness framework grounded in the concept of happiness, a measure of the utility each group gains fromdecisionoutcomes. Bycapturingfairness 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Happiness as a Measure of Fairness Pichler, Georg Romanelli, Marco Piantanida, Pablo Machine Learning In this paper, we propose a novel fairness framework grounded in the concept of happiness, a measure of the utility each group gains fromdecisionoutcomes. Bycapturingfairness 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. |
| title | Happiness as a Measure of Fairness |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.01069 |