mlsanityData to 7 Oct 2026

Individually Fair Ranking

arXiv 2103.11023 ↗First posted 19 Mar 2021stat.ML cs.LGcited by 7

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Abstract (arXiv)

We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups. This notion of fair ranking is based on the definition of individual fairness from supervised learning and is more nuanced than prior fair LTR approaches that simply ensure the ranking model provides underrepresented items with a basic level of exposure. The crux of our method is an optimal transport-based regularizer that enforces individual fairness and an efficient algorithm for optimizing the regularizer. We show that our approach leads to certifiably individually fair LTR models and demonstrate the efficacy of our method on ranking tasks subject to demographic biases.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 28 of its 39 references that are in the map. First posted on arXiv 19 Mar 2021.

Builds on

28 of 39 references.

References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2017Counterfactual Fairnesscited by 802
  4. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  5. 2018Adversarial Logit Pairingcited by 224
  6. 2018Fairness of Exposure in Rankingscited by 189
  7. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 150
  8. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  9. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  10. 2013Introducing LETOR 4.0 Datasetscited by 120
  11. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  12. 2016Measuring Fairness in Ranked Outputscited by 81
16 more
  1. 2017Ranking with Fairness Constraintscited by 78
  2. 2019Policy Learning for Fairness in Rankingcited by 74
  3. 2019Training individually fair ML models with Sensitive Subspace Robustnesscited by 73
  4. 2020Two Simple Ways to Learn Individual Fairness Metrics from Datacited by 54
  5. 2019Metric Learning for Individual Fairnesscited by 51
  6. 2017Designing Fair Ranking Schemescited by 40
  7. 2020SenSeI: Sensitive Set Invariance for Enforcing Individual Fairnesscited by 37
  8. 2020Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approachcited by 32
  9. 2020Interventions for Ranking in the Presence of Implicit Biascited by 27
  10. 2019Quantifying the Impact of User Attention on Fair Group Representation in Ranked Listscited by 24
  11. 2019FairSearch: A Tool For Fairness in Ranked Search Resultscited by 22
  12. 2019Balanced Ranking with Diversity Constraintscited by 20
  13. 2019Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustnesscited by 18
  14. 2018On Discrimination Discovery and Removal in Ranked Data using Causal Graphcited by 16
  15. 2019An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervisioncited by 13
  16. 2017On conditional parity as a notion of non-discrimination in machine learningcited by 10

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Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 1.

7 papers.

  1. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  2. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  3. 2022De-biasing "bias" measurementcited by 20
  4. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  5. 2023Fairness in Matching under Uncertaintycited by 5
  6. 2023Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcitycited by 1
  7. 2024Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averagescited by 1