How to ensure fairness in algorithmic decision making models is an important task in machine learning [12,15]. Over the past years, many researchers have been devoted to the design of fair classification algorithms withrespecttoapre-defined protected attribute,suchasraceorsex,anda decision task/model, such as hiring [1,11,24]. In particular,one line of the work istoincorporate fairness constraints into classic learning algorithms tobuild fair classifiers from potentially biased data [4,13,29,31-33]. Most of previous research generally focuses on a single decision model.

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 context. Our formalism allows us to design a method 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.