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We study a novel problem of fairness in ranking aimed at minimizing theamount of individual unfairness introduced when enforcing group-fairnessconstraints. Our proposal is rooted in the distributional maxmin fairnesstheory, which uses randomization to maximize the expected satisfaction of theworst-off individuals. We devise an exact polynomial-time algorithm to findmaxmin-fair distributions of general search problems (including, but notlimited to, ranking), and show that our algorithm can produce rankings which,while satisfying the given group-fairness constraints, ensure that the maximumpossible value is brought to individuals.

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