mlsanityData to 7 Oct 2026

Optimizing generalized Gini indices for fairness in rankings

arXiv 2204.06521 ↗First posted 2 Apr 2022cs.IR cs.AI cs.CYcited by 13

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Abstract (arXiv)

There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 37 of its 86 references that are in the map. First posted on arXiv 2 Apr 2022.

Builds on

37 of 86 references.

References · 37

  1. 2020Fast Differentiable Sorting and Rankingcited by 196
  2. 2018Fairness of Exposure in Rankingscited by 189
  3. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  4. 2019Differentiable Ranks and Sorting using Optimal Transportcited by 113
  5. 2019Stochastic Optimization of Sorting Networks via Continuous Relaxationscited by 111
  6. 2018A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indicescited by 97
  7. 2013Discrimination in Online Ad Deliverycited by 93
  8. 2019Fairness risk measurescited by 85
  9. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  10. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  11. 2017Multisided Fairness for Recommendationcited by 69
  12. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
25 more
  1. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 67
  2. 2020Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewardscited by 60
  3. 2020Evaluating Stochastic Rankings with Expected Exposurecited by 53
  4. 2018Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Makingcited by 46
  5. 2019Fair Classification and Social Welfarecited by 40
  6. 2020Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learningcited by 40
  7. 2018Reducing Disparate Exposure in Ranking: A Learning To Rank Approachcited by 33
  8. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  9. 2020Bridging Machine Learning and Mechanism Design towards Algorithmic Fairnesscited by 30
  10. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  11. 2013The Complexity of Large-scale Convex Programming under a Linear Optimization Oraclecited by 27
  12. 2017Multi-objective Bandits: Optimizing the Generalized Gini Indexcited by 27
  13. 2020User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Marketscited by 23
  14. 2013The Anatomy of a Scientific Rumorcited by 22
  15. 2021Online certification of preference-based fairness for personalized recommender systemscited by 22
  16. 2021An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learningcited by 18
  17. 2020Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Methodcited by 17
  18. 2018A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programmingcited by 14
  19. 2017Auditing Search Engines for Differential Satisfaction Across Demographicscited by 12
  20. 2021Optimizing Rankings for Recommendation in Matching Marketscited by 12
  21. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
  22. 2022Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metricscited by 8
  23. 2015Reciprocal Recommendation System for Online Datingcited by 7
  24. 2020A Framework for Fairness in Two-Sided Marketplacescited by 6
  25. 2017A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guaranteescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 5; 2025: 1; 2026: 3.

13 papers.

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  1. 2023Fairness in Matching under Uncertaintycited by 5
  2. 2024Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systemscited by 3
  3. 2022Contextual bandits with concave rewards, and an application to fair rankingcited by 2
  4. 2024End-to-End Learning for Fair Multiobjective Optimization Under Uncertaintycited by 2
  5. 2026Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learningcited by 2
  6. 2024Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averagescited by 1
  7. 2024AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spacescited by 1
  8. 2023Learning to Re-rank with Constrained Meta-Optimal Transportcited by 0
  9. 2023FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resourcescited by 0
  10. 2024End-to-End Optimization and Learning of Fair Court Schedulescited by 0
  11. 2025Toward Unifying Group Fairness Evaluation from a Sparsity Perspectivecited by 0
  12. 2026Contrastive Learning for Diversity-Aware Product Recommendations in Retailcited by 0
1 more
  1. 2026Inference-Time Policy Alignment for Fair Reinforcement Learningcited by 0