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

The bulk of the algorithmic fairness literature deals with group fairness along the lines of demographic parity [9] or equal opportunity We study a novel problem of fairness in ranking aimed at minimizing [16]: this is typically expressed by means of some fairness the amount of individual unfairness introduced when enforcing constraint requiring that the top-positions (for any) in the ranking group-fairness constraints. Our proposal is rooted in the contain enough elements from some groups that are protected distributional maxmin fairness theory, which uses randomization from discrimination based on sex, race, age, etc. In fact, [6] shows to maximize the expected satisfaction of the worst-off individuals.