mlsanityData to 7 Oct 2026

FA*IR: A Fair Top-k Ranking Algorithm

arXiv 1706.06368 ↗First posted 20 Jun 2017cs.CY cs.IRcited by 113

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Abstract (arXiv)

In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria. Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-k should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above. An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.

Key facts. Cited by 113 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2017.

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Citing papers per year: 2021: 25; 2022: 25; 2023: 16; 2024: 16; 2025: 15; 2026: 16.

113 papers.

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  1. 2022Fair and Optimal Classification via Post-Processingcited by 50
  2. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  3. 2021Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankerscited by 34
  4. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  5. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 32
  6. 2021Fairness in Ranking under Uncertaintycited by 27
  7. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  8. 2021When Fair Ranking Meets Uncertain Inferencecited by 22
  9. 2022Joint Multisided Exposure Fairness for Recommendationcited by 22
  10. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  11. 2021Strategic Rankingcited by 21
  12. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
38 more
  1. 2022De-biasing "bias" measurementcited by 20
  2. 2021Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraintscited by 18
  3. 2021End-to-end Learning for Fair Ranking Systemscited by 18
  4. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  5. 2024Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankerscited by 17
  6. 2024Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generationcited by 15
  7. 2021Estimation of Fair Ranking Metrics with Incomplete Judgmentscited by 14
  8. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  9. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  10. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
  11. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  12. 2024Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impactcited by 9
  13. 2021Fairness Through Regularization for Learning to Rankcited by 8
  14. 2022Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metricscited by 8
  15. 2022Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systemscited by 8
  16. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  17. 2021Individually Fair Rankingcited by 7
  18. 2021Pairwise Fairness for Ordinal Regressioncited by 6
  19. 2021Identifying Biased Subgroups in Ranking and Classificationcited by 6
  20. 2021Fairness for Robust Learning to Rankcited by 6
  21. 2026CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMscited by 6
  22. 2022Subverting Fair Image Search with Generative Adversarial Perturbationscited by 5
  23. 2022Debiasing Neural Retrieval via In-batch Balancing Regularizationcited by 5
  24. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  25. 2023Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustmentcited by 5
  26. 2024Language Fairness in Multilingual Information Retrievalcited by 5
  27. 2025Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrievalcited by 5
  28. 2021A Ranking Approach to Fair Classificationcited by 4
  29. 2022Unintended Bias in Language Model-driven Conversational Recommendationcited by 4
  30. 2022Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Rankingcited by 4
  31. 2022Towards Fairness-Aware Multi-Objective Optimizationcited by 4
  32. 2023Fair Column Subset Selectioncited by 4
  33. 2023Fairness in Ranking under Disparate Uncertaintycited by 4
  34. 2024Measuring Bias in a Ranked List using Term-based Representationscited by 4
  35. 2025How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Modelscited by 4
  36. 2021On Meritocracy in Optimal Set Selectioncited by 3
  37. 2021A Pre-processing Method for Fairness in Rankingcited by 3
  38. 2022Fair Interpretable Representation Learning with Correction Vectorscited by 3

50 most cited of 113 All 113 (JSON)