mlsanityData to 7 Oct 2026

Fairness in Rankings and Recommendations: An Overview

arXiv 2104.05994 ↗First posted 13 Apr 2021cs.DBcited by 62

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Abstract (arXiv)

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this work, we aim at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are three-fold: (a) to provide a solid framework on a novel, quickly evolving, and impactful domain, (b) to present related methods and put them into perspective, and (c) to highlight open challenges and research paths for future work.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2021.

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Citing papers per year: 2021: 3; 2022: 14; 2023: 12; 2024: 11; 2025: 14; 2026: 8.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  3. 2022Fairness in Graph Mining: A Surveycited by 76
  4. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  5. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  6. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  7. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  8. 2022Fair Ranking with Noisy Protected Attributescited by 17
  9. 2023Multi-dimensional discrimination in Law and Machine Learning -- A comparative overviewcited by 15
  10. 2024New Job, New Gender? Measuring the Social Bias in Image Generation Modelscited by 15
  11. 2023Recent Developments in Recommender Systems: A Surveycited by 14
  12. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
38 more
  1. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  2. 2023GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hintcited by 6
  3. 2024Measuring and Addressing Indexical Bias in Information Retrievalcited by 6
  4. 2022Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathwayscited by 5
  5. 2023Maximizing Submodular Functions for Recommendation in the Presence of Biasescited by 5
  6. 2024Towards Standardizing AI Bias Explorationcited by 5
  7. 2024Stability and Multigroup Fairness in Ranking with Uncertain Predictionscited by 4
  8. 2021A Pre-processing Method for Fairness in Rankingcited by 3
  9. 2022Models and Mechanisms for Spatial Data Fairnesscited by 3
  10. 2024On Explaining Unfairness: An Overviewcited by 3
  11. 2024FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairnesscited by 3
  12. 2026The Price of Proportional Representation in Temporal Votingcited by 3
  13. 2021Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunitiescited by 2
  14. 2022Properties of Group Fairness Metrics for Rankingscited by 2
  15. 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
  16. 2023Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functionscited by 2
  17. 2023Sampling Individually-Fair Rankings that are Always Group Faircited by 2
  18. 2023"I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorialscited by 2
  19. 2024Multi-Group Proportional Representation in Retrievalcited by 2
  20. 2025Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Explorationcited by 2
  21. 2025CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairnesscited by 2
  22. 2022Introducing a Framework and a Decision Protocol to Calibrate Recommender Systemscited by 1
  23. 2022RAGUEL: Recourse-Aware Group Unfairness Eliminationcited by 1
  24. 2023Matrix Estimation for Individual Fairnesscited by 1
  25. 2023Auditing for Spatial Fairnesscited by 1
  26. 2023Seller-side Outcome Fairness in Online Marketplacescited by 1
  27. 2024Fast solution to the fair ranking problem using the Sinkhorn algorithmcited by 1
  28. 2025Recommending the right academic programs: An interest mining approach using BERTopiccited by 1
  29. 2025Evaluating Large Language Models for Fair and Reliable Organ Allocationcited by 1
  30. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  31. 2025Promoting Fairness in Information Access within Social Networkscited by 1
  32. 2026Explaining Group Recommendations via Counterfactualscited by 1
  33. 2026Efficient Edge Rewiring Strategies for Enhancing PageRank Fairnesscited by 1
  34. 2026Measuring Individual User Fairness with User Similarity and Effectiveness Disparitycited by 1
  35. 2022Detection of Groups with Biased Representation in Rankingcited by 0
  36. 2023The Initial Screening Order Problemcited by 0
  37. 2024Fair Active Ranking from Pairwise Preferencescited by 0
  38. 2024Reducing Biases in Record Matching Through Scores Calibrationcited by 0

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