mlsanityData to 7 Oct 2026

Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints

arXiv 2106.08652 ↗First posted 16 Jun 2021cs.LG cs.AI cs.DScited by 18

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Abstract (arXiv)

We study a novel problem of fairness in ranking aimed at minimizing the amount of individual unfairness introduced when enforcing group-fairness constraints. Our proposal is rooted in the distributional maxmin fairness theory, which uses randomization to maximize the expected satisfaction of the worst-off individuals. We devise an exact polynomial-time algorithm to find maxmin-fair distributions of general search problems (including, but not limited to, ranking), and show that our algorithm can produce rankings which, while satisfying the given group-fairness constraints, ensure that the maximum possible value is brought to individuals.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 13 of its 36 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

13 of 36 references.

References · 13

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2018Fairness of Exposure in Rankingscited by 189
  3. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  4. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  5. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  6. 2016Measuring Fairness in Ranked Outputscited by 81
  7. 2017Ranking with Fairness Constraintscited by 78
  8. 2019Policy Learning for Fairness in Rankingcited by 74
  9. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  10. 2017Designing Fair Ranking Schemescited by 40
  11. 2020Interventions for Ranking in the Presence of Implicit Biascited by 27
  12. 2019Balanced Ranking with Diversity Constraintscited by 20
1 more
  1. 2018Fair-by-design matchingcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 3; 2024: 3; 2025: 5; 2026: 1.

18 papers.

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  1. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  2. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  3. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  4. 2022Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioningcited by 4
  5. 2024Stability and Multigroup Fairness in Ranking with Uncertain Predictionscited by 4
  6. 2024A Catalog of Fairness-Aware Practices in Machine Learning Engineeringcited by 4
  7. 2022Balancing Utility and Fairness in Submodular Maximization (Technical Report)cited by 2
  8. 2023On the Within-Group Fairness of Screening Classifierscited by 2
  9. 2023Sampling Individually-Fair Rankings that are Always Group Faircited by 2
  10. 2023Boosting Fair Classifier Generalization through Adaptive Priority Reweighingcited by 1
  11. 2025What's in a Query: Polarity-Aware Distribution-Based Fair Rankingcited by 1
  12. 2025Size-adaptive Hypothesis Testing for Fairnesscited by 1
6 more
  1. 2022Individual Fairness under Varied Notions of Group Fairness in Bipartite Matching - One Framework to Approximate Them Allcited by 0
  2. 2024Individual Fairness Through Reweighting and Tuningcited by 0
  3. 2025FairUDT: Fairness-aware Uplift Decision Treescited by 0
  4. 2025Quantifying Misattribution Unfairness in Authorship Attributioncited by 0
  5. 2025Rawlsian many-to-one matching with non-linear utilitycited by 0
  6. 2026Continuous Fairness On Data Streamscited by 0