---
_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'
...