--- res: bibo_abstract: - 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.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Nikola H foaf_name: Konstantinov, Nikola H foaf_surname: Konstantinov foaf_workInfoHomepage: http://www.librecat.org/personId=4B9D76E4-F248-11E8-B48F-1D18A9856A87 - foaf_Person: foaf_givenName: Christoph foaf_name: Lampert, Christoph foaf_surname: Lampert foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-4561-241X bibo_doi: 10.48550/arXiv.2102.05996 dct_date: 2021^xs_gYear dct_language: eng dct_title: Fairness through regularization for learning to rank@ ...