Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints
This addresses fairness in ranking systems, balancing individual and group fairness, but appears incremental as it builds on existing maxmin fairness theory.
The paper tackles the problem of minimizing individual unfairness when enforcing group-fairness constraints in ranking, proposing a maxmin-fair ranking approach that uses randomization to maximize expected satisfaction for the worst-off individuals, with an exact polynomial-time algorithm developed to find such distributions.
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.