  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.
