mlsanityData to 7 Oct 2026

Fairness Through Regularization for Learning to Rank

arXiv 2102.05996 ↗First posted 11 Feb 2021cs.LG cs.IR stat.MLcited by 8

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Abstract (arXiv)

Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalization guarantees. An extensive experimental evaluation shows that our method can improve ranking fairness substantially with no or only little loss of model quality.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 39 of its 78 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

39 of 78 references.

References · 39

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  5. 2017Counterfactual Fairnesscited by 802
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2019Passage Re-ranking with BERTcited by 629
  8. 2018A Reductions Approach to Fair Classificationcited by 585
  9. 2018Fairness of Exposure in Rankingscited by 189
  10. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  11. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  12. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
27 more
  1. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  2. 2019Fairness risk measurescited by 85
  3. 2016Measuring Fairness in Ranked Outputscited by 81
  4. 2017Ranking with Fairness Constraintscited by 78
  5. 2019Policy Learning for Fairness in Rankingcited by 74
  6. 2017Multisided Fairness for Recommendationcited by 69
  7. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 67
  8. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  9. 2018Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraintscited by 53
  10. 2019The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metriccited by 41
  11. 2017Designing Fair Ranking Schemescited by 40
  12. 2020Learning-to-Rank with BERT in TF-Rankingcited by 36
  13. 2018Reducing Disparate Exposure in Ranking: A Learning To Rank Approachcited by 33
  14. 2019R'enyi Fair Inferencecited by 33
  15. 2020Interventions for Ranking in the Presence of Implicit Biascited by 27
  16. 2019Quantifying the Impact of User Attention on Fair Group Representation in Ranked Listscited by 24
  17. 2019FairSearch: A Tool For Fairness in Ranked Search Resultscited by 22
  18. 2019Balanced Ranking with Diversity Constraintscited by 20
  19. 2018A Fairness-aware Hybrid Recommender Systemcited by 18
  20. 2019Learning Fair Representations for Kernel Modelscited by 17
  21. 2018On Discrimination Discovery and Removal in Ranked Data using Causal Graphcited by 16
  22. 2018Bias Disparity in Recommendation Systemscited by 12
  23. 2018Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendationscited by 11
  24. 2020Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraintscited by 7
  25. 2020Overview of the TREC 2019 Fair Ranking Trackcited by 4
  26. 2020On the Problem of Underranking in Group-Fair Rankingcited by 2
  27. 2019Fair Division Without Disparate Impactcited by 1

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 0; 2025: 3.

8 papers.

  1. 2022FARE: Provably Fair Representation Learning with Practical Certificatescited by 21
  2. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  3. 2022Debiasing Neural Retrieval via In-batch Balancing Regularizationcited by 5
  4. 2025Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspectivecited by 5
  5. 2023Fairness in Ranking under Disparate Uncertaintycited by 4
  6. 2023Using Property Elicitation to Understand the Impacts of Fairness Regularizerscited by 0
  7. 2025On the Correlation between Individual Fairness and Predictive Accuracy in Probabilistic Modelscited by 0
  8. 2025Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knobcited by 0