Happiness as a Measure of Fairness
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 2 Nov 2025.
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References · 18
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2018A Reductions Approach to Fair Classificationcited by 587
- 2017On Fairness and Calibrationcited by 406
- 2020Fairness in Machine Learning: A Surveycited by 370
- 2020Performative Predictioncited by 327
- 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
- 2018Delayed Impact of Fair Machine Learningcited by 184
- 2021A survey on datasets for fairness-aware machine learningcited by 157
- 2018Fairness Through Computationally-Bounded Awarenesscited by 55
- 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
- 2024FairProof : Confidential and Certifiable Fairness for Neural Networkscited by 13
6 more
- 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
- 2022Enforcing Delayed-Impact Fairness Guaranteescited by 8
- 2023Learning Optimal Fair Scoring Systems for Multi-Class Classificationcited by 8
- 202410 Years of Fair Representations: Challenges and Opportunitiescited by 6
- 2022Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictorscited by 5
- 2024Regression under demographic parity constraints via unlabeled post-processingcited by 5
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