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:
SIGMOD '23: Companion of the 2023 International Conference on Management of Data
影响因子:
--
通讯作者:
Yang, Ke
Yang, Ke
中科院分区:
--
文献类型:
--
作者:
Stoyanovich, Julia;Zehlike, Meike;Yang, Ke

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在过去的几年里,已经有很多工作将公平性要求融入到算法排名器的设计中,数据管理、算法、信息检索和推荐系统社区做出了贡献。在本教程中,我们系统地概述了这项工作,提供了一个广泛的视角,将形式化和算法方法跨子领域联系起来。在教程的第一部分,我们提出了一个促进公平的干预的分类框架,然后我们将沿着这个框架来描述技术方法。这一框架使我们能够统一提出缓解目标和算法技术,以帮助实现这些目标或确定权衡。接下来,我们讨论基于分数的排名和有监督的学习排名中的公平性。最后,我们对从业者提出了建议,帮助他们根据自己特定应用领域的需求选择一种公平的排名方法。
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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影响因子: --
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