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.

翻译：在过去几年里,在将公平要求纳入算法排级器方面做了大量工作,数据管理、算法、信息检索和建议系统界的贡献。在这次调查中,我们系统地概述了这项工作,提供了将各子领域正规化和算法方法联系起来的广泛观点。我们工作的一个重要贡献是围绕价值框架制定一个共同的叙述,鼓励在排名中采取具体的促进公平的措施。这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找出取舍。