In the past few years, there has been much work on incorporating fairness
requirements into algorithmic rankers, with contributions coming from the data
management, algorithms, information retrieval, and recommender systems
communities. In this survey we give a systematic overview of this work,
offering a broad perspective that connects formalizations and algorithmic
approaches across subfields. An important contribution of our work is in
developing a common narrative around the value frameworks that motivate
specific fairness-enhancing interventions in ranking. This allows us to unify
the presentation of mitigation objectives and of algorithmic techniques to help
meet those objectives or identify trade-offs. In this survey, we describe four
classification frameworks for fairness-enhancing interventions, along which we
relate the technical methods surveyed in this paper, discuss evaluation
datasets, and present technical work on fairness in score-based ranking. Then,
we present methods that incorporate fairness in supervised learning, and also
give representative examples of recent work on fairness in recommendation and
matchmaking systems. We also discuss evaluation frameworks for fair score-based
ranking and fair learning-to-rank, and draw a set of recommendations for the
evaluation of fair ranking methods.

本文对过去几年来有关将公平性要求纳入算法排名器的工作进行了系统概述，提供了跨学科的算法形式化和方法连接的广阔视角，并针对公平性增强干预分类架构描述了四个分类框架，并讨论了公平排序评估数据集和技术工作。我们还讨论了公平分数排序和公平学习排序的评估框架，并得出了有关公平排名方法评估的一系列建议。