Happiness as a Measure of Fairness
Georg Pichler, Marco Romanelli, Pablo Piantanida

TL;DR
This paper introduces a new fairness framework based on happiness, measuring group utility from decision outcomes, which is both human-centered and mathematically rigorous, enabling efficient optimization and unification of existing fairness notions.
Contribution
The paper presents a novel happiness-based fairness framework that is scalable, unifies existing fairness definitions, and is grounded in a simple linear programming approach.
Findings
Efficient linear program computes optimal fair strategies.
Unifies multiple fairness definitions under a single framework.
Demonstrates practical effectiveness across diverse scenarios.
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.
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Taxonomy
TopicsEthics and Social Impacts of AI · Game Theory and Voting Systems · Experimental Behavioral Economics Studies
