--- _id: '10803' abstract: - lang: eng text: 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. article_number: '2102.05996' article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X citation: ama: Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv. doi:10.48550/arXiv.2102.05996 apa: Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996 chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2102.05996. ieee: N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. . ista: Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996. mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” ArXiv, 2102.05996, doi:10.48550/arXiv.2102.05996. short: N.H. Konstantinov, C. Lampert, ArXiv (n.d.). date_created: 2022-02-28T14:13:59Z date_published: 2021-06-07T00:00:00Z date_updated: 2023-09-07T13:42:08Z day: '07' department: - _id: ChLa doi: 10.48550/arXiv.2102.05996 external_id: arxiv: - '2102.05996' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.05996 month: '06' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted related_material: record: - id: '10799' relation: dissertation_contains status: public status: public title: Fairness through regularization for learning to rank type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ...