Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints

TL;DR

This paper introduces a polynomial-time algorithm for fair ranking that balances individual fairness with group fairness constraints, ensuring the worst-off individuals are maximally satisfied.

Contribution

It presents a novel maxmin-fairness framework for ranking that guarantees individual fairness under group constraints using an exact polynomial-time algorithm.

Findings

Algorithm efficiently finds maxmin-fair distributions.

Rankings satisfy group fairness while maximizing individual satisfaction.

Applicable to various search problems beyond ranking.

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.