mlsanityData to 7 Oct 2026

Fairness of Exposure in Rankings

arXiv 1802.07281 ↗First posted 20 Feb 2018cs.IR cs.CYcited by 189

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Abstract (arXiv)

Rankings are ubiquitous in the online world today. As we have transitioned from finding books in libraries to ranking products, jobs, job applicants, opinions and potential romantic partners, there is a substantial precedent that ranking systems have a responsibility not only to their users but also to the items being ranked. To address these often conflicting responsibilities, we propose a conceptual and computational framework that allows the formulation of fairness constraints on rankings in terms of exposure allocation. As part of this framework, we develop efficient algorithms for finding rankings that maximize the utility for the user while provably satisfying a specifiable notion of fairness. Since fairness goals can be application specific, we show how a broad range of fairness constraints can be implemented using our framework, including forms of demographic parity, disparate treatment, and disparate impact constraints. We illustrate the effect of these constraints by providing empirical results on two ranking problems.

Key facts. Cited by 189 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2018.

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Citing papers per year: 2021: 44; 2022: 47; 2023: 30; 2024: 20; 2025: 17; 2026: 31.

189 papers.

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  1. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  2. 2023Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunitiescited by 129
  3. 2021User-oriented Fairness in Recommendationcited by 75
  4. 2021AutoDebias: Learning to Debias for Recommendationcited by 68
  5. 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
  6. 2021Characterizing Intersectional Group Fairness with Worst-Case Comparisonscited by 42
  7. 2021Fairness of Exposure in Stochastic Banditscited by 42
  8. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  9. 2021Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankerscited by 34
  10. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  11. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  12. 2021Fairness in Ranking under Uncertaintycited by 27
38 more
  1. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  2. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  3. 2021Online certification of preference-based fairness for personalized recommender systemscited by 22
  4. 2021When Fair Ranking Meets Uncertain Inferencecited by 22
  5. 2022Joint Multisided Exposure Fairness for Recommendationcited by 22
  6. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  7. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  8. 2021Measuring Recommender System Effects with Simulated Userscited by 20
  9. 2021Technical Challenges for Training Fair Neural Networkscited by 20
  10. 2021Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloudcited by 19
  11. 2021Loss-Aversively Fair Classificationcited by 18
  12. 2021Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraintscited by 18
  13. 2021End-to-end Learning for Fair Ranking Systemscited by 18
  14. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  15. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  16. 2022Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rankcited by 17
  17. 2022Fair Ranking with Noisy Protected Attributescited by 17
  18. 2024Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankerscited by 17
  19. 2024Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generationcited by 15
  20. 2021Estimation of Fair Ranking Metrics with Incomplete Judgmentscited by 14
  21. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  22. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  23. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  24. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  25. 2022Fair Ranking as Fair Division: Impact-Based Individual Fairness in Rankingcited by 13
  26. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  27. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  28. 2021Bandit Algorithms for Precision Medicinecited by 12
  29. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  30. 2022Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Modelscited by 12
  31. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  32. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
  33. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  34. 2022RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systemscited by 10
  35. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  36. 2021Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilitiescited by 9
  37. 2021Fairness Through Regularization for Learning to Rankcited by 8
  38. 2021Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachabilitycited by 8

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