mlsanityData to 7 Oct 2026

Fairness for Robust Learning to Rank

arXiv 2112.06288 ↗First posted 12 Dec 2021cs.LG cs.CY stat.MLcited by 6

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Abstract (arXiv)

While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race. To achieve this type of group fairness for ranking, we derive a new ranking system based on the first principles of distributional robustness. We formulate a minimax game between a player choosing a distribution over rankings to maximize utility while satisfying fairness constraints against an adversary seeking to minimize utility while matching statistics of the training data. We show that our approach provides better utility for highly fair rankings than existing baseline methods.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 58 references that are in the map. First posted on arXiv 12 Dec 2021.

Builds on

31 of 58 references.

References · 31

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  4. 2018A Reductions Approach to Fair Classificationcited by 585
  5. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  6. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  7. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  8. 2018Fairness of Exposure in Rankingscited by 189
  9. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  10. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  11. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  12. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
19 more
  1. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  2. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  3. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  4. 2016Measuring Fairness in Ranked Outputscited by 81
  5. 2017Ranking with Fairness Constraintscited by 78
  6. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  7. 2019Policy Learning for Fairness in Rankingcited by 74
  8. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 67
  9. 2017From Parity to Preference-based Notions of Fairness in Classificationcited by 60
  10. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  11. 2020Robust Fairness under Covariate Shiftcited by 58
  12. 2019The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metriccited by 41
  13. 2017Designing Fair Ranking Schemescited by 40
  14. 2018Reducing Disparate Exposure in Ranking: A Learning To Rank Approachcited by 33
  15. 2020Interventions for Ranking in the Presence of Implicit Biascited by 27
  16. 2019Improved Adversarial Learning for Fair Classificationcited by 25
  17. 2019Quantifying the Impact of User Attention on Fair Group Representation in Ranked Listscited by 24
  18. 2020A Framework for Fairness in Two-Sided Marketplacescited by 6
  19. 2020On the Problem of Underranking in Group-Fair Rankingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

6 papers.

  1. 2022Fair Ranking with Noisy Protected Attributescited by 17
  2. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  3. 2023Sampling Individually-Fair Rankings that are Always Group Faircited by 2
  4. 2022Sampling Ex-Post Group-Fair Rankingscited by 0
  5. 2025Learning to Rank with Top-K Fairnesscited by 0
  6. 2026Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Rankingcited by 0