Happiness as a Measure of Fairness

Georg Pichler, Marco Romanelli, Pablo Piantanida

TL;DR: We propose a new fairness framework based on group utility, addressing limitations of existing post-processing and generalizing prior fairness definitions.

Abstract: In this paper, we propose a novel fairness framework grounded in the concept of _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.

Submission Number: 89