Fairness in Ranking: From Values to Technical Choices and Back
Fairness in Ranking: From Values to Technical Choices and Back
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排名的公平性:从价值观到技术选择并返回
DOI:
10.1145/3555041.3589405
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Yang, Ke
中科院分区:
文献类型:
--
作者:
Stoyanovich, Julia;Zehlike, Meike;Yang, Ke
In the past few years, there has been much work on incorporating fairness requirements into the design of algorithmic rankers, with contributions from the data management, algorithms, information retrieval, and recommender systems communities. In this tutorial, we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields.During the first part of the tutorial, we present a classification framework for fairness-enhancing interventions, along which we will then relate the technical methods. This framework allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. Next, we discuss fairness in score-based ranking and in supervised learning-to-rank. We conclude with recommendations for practitioners, to help them select a fair ranking method based on the requirements of their specific application domain.
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DOI:
10.1145/3366424.3383534
发表时间:
2018-05
期刊:
Companion Proceedings of the Web Conference 2020
影响因子:
--
作者:
Meike Zehlike;Tom Sühr;C. Castillo;Ivan Kitanovski
通讯作者:
Meike Zehlike;Tom Sühr;C. Castillo;Ivan Kitanovski
DOI:
10.3726/b16930
发表时间:
2020
期刊:
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
影响因子:
--
作者:
Evgeny Soshkin
通讯作者:
Evgeny Soshkin
DOI:
10.4230/lipics.itcs.2018.33
发表时间:
2018
期刊:
ArXiv
影响因子:
--
作者:
J. Kleinberg;Manish Raghavan
通讯作者:
Manish Raghavan
DOI:
10.4230/lipics.icalp.2018.28
发表时间:
2017-04
期刊:
ArXiv
影响因子:
--
作者:
L. E. Celis;D. Straszak;Nisheeth K. Vishnoi
通讯作者:
L. E. Celis;D. Straszak;Nisheeth K. Vishnoi
影响因子:
16.6
作者:
Meike Zehlike;Ke Yang;Julia Stoyanovich
通讯作者:
Meike Zehlike;Ke Yang;Julia Stoyanovich