Fairness in Ranking, Part I: Score-Based Ranking

Meike Zehlike, Ke Yang, Julia Stoyanovich

    Research output: Contribution to journalArticlepeer-review


    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 sub-fields. 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 first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this article, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In the second part of this survey, 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.

    Original languageEnglish (US)
    Article number3533379
    JournalACM Computing Surveys
    Issue number6
    StatePublished - Dec 7 2022


    • Fairness
    • ranking
    • responsible data science
    • set selection
    • survey

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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