  Given the abundance of applications of ranking in recent years, addressing
fairness concerns around automated ranking systems becomes necessary for
increasing the trust among end-users. Previous work on fair ranking has mostly
focused on application-specific fairness notions, often tailored to online
advertising, and it rarely considers learning as part of the process. In this
work, we show how to transfer numerous fairness notions from binary
classification to a learning to rank setting. Our formalism allows us to design
methods for incorporating fairness objectives with provable generalization
guarantees. An extensive experimental evaluation shows that our method can
improve ranking fairness substantially with no or only little loss of model
quality.
