Happiness as a Measure of Fairness

2025/11/02 by Pichler, Georg, Romanelli, Marco, Piantanida, Pablo
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.2511.01069

Abstract

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.

Citations

• Regression under demographic parity constraints via unlabeled post-processing
• 10 Years of Fair Representations: Challenges and Opportunities
• FairProof : Confidential and Certifiable Fairness for Neural Networks
• Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
• Enforcing Delayed-Impact Fairness Guarantees
• Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors
• Performative Prediction
• A Survey on Bias and Fairness in Machine Learning
• Obtaining fairness using optimal transport theory
• Fairness Through Computationally-Bounded Awareness
• A Reductions Approach to Fair Classification
• On Fairness and Calibration
• Fairness in Criminal Justice Risk Assessments: The State of the Art
• Fairness in Criminal Justice Risk Assessments: The State of the Art
• Equality of Opportunity in Supervised Learning
• Fairness Through Awareness

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