Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints
Machine Learning
2021-06-18 v2 Artificial Intelligence
Data Structures and Algorithms
Abstract
We study a novel problem of fairness in ranking aimed at minimizing the amount of individual unfairness introduced when enforcing group-fairness constraints. Our proposal is rooted in the distributional maxmin fairness theory, which uses randomization to maximize the expected satisfaction of the worst-off individuals. We devise an exact polynomial-time algorithm to find maxmin-fair distributions of general search problems (including, but not limited to, ranking), and show that our algorithm can produce rankings which, while satisfying the given group-fairness constraints, ensure that the maximum possible value is brought to individuals.
Keywords
Cite
@article{arxiv.2106.08652,
title = {Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints},
author = {David Garcia-Soriano and Francesco Bonchi},
journal= {arXiv preprint arXiv:2106.08652},
year = {2021}
}
Comments
In proceedings of KDD 2021