# Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints

arXiv: https://arxiv.org/abs/2106.08652 · HTML: https://mlsanity.com/paper/2106.08652/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Measuring Fairness in Ranked Outputs](https://mlsanity.com/paper/1610.08559/) (2016)
- [Ranking with Fairness Constraints](https://mlsanity.com/paper/1704.06840/) (2017)
- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [Pairwise Fairness for Ranking and Regression](https://mlsanity.com/paper/1906.05330/) (2019)
- [Designing Fair Ranking Schemes](https://mlsanity.com/paper/1712.09752/) (2017)
- [Interventions for Ranking in the Presence of Implicit Bias](https://mlsanity.com/paper/2001.08767/) (2020)
- [Balanced Ranking with Diversity Constraints](https://mlsanity.com/paper/1906.01747/) (2019)
- [Fair-by-design matching](https://mlsanity.com/paper/1802.02562/) (2018)

## Built on by (most cited first, 18 of 18)

- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [Modeling Techniques for Machine Learning Fairness: A Survey](https://mlsanity.com/paper/2111.03015/) (2021)
- [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022)
- [Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning](https://mlsanity.com/paper/2210.07770/) (2022)
- [Stability and Multigroup Fairness in Ranking with Uncertain Predictions](https://mlsanity.com/paper/2402.09326/) (2024)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Balancing Utility and Fairness in Submodular Maximization (Technical Report)](https://mlsanity.com/paper/2211.00980/) (2022)
- [On the Within-Group Fairness of Screening Classifiers](https://mlsanity.com/paper/2302.00025/) (2023)
- [Sampling Individually-Fair Rankings that are Always Group Fair](https://mlsanity.com/paper/2306.11964/) (2023)
- [Boosting Fair Classifier Generalization through Adaptive Priority Reweighing](https://mlsanity.com/paper/2309.08375/) (2023)
- [What's in a Query: Polarity-Aware Distribution-Based Fair Ranking](https://mlsanity.com/paper/2502.11429/) (2025)
- [Size-adaptive Hypothesis Testing for Fairness](https://mlsanity.com/paper/2506.10586/) (2025)
- [Individual Fairness under Varied Notions of Group Fairness in Bipartite Matching - One Framework to Approximate Them All](https://mlsanity.com/paper/2208.09951/) (2022)
- [Individual Fairness Through Reweighting and Tuning](https://mlsanity.com/paper/2405.01711/) (2024)
- [FairUDT: Fairness-aware Uplift Decision Trees](https://mlsanity.com/paper/2502.01188/) (2025)
- [Quantifying Misattribution Unfairness in Authorship Attribution](https://mlsanity.com/paper/2506.02321/) (2025)
- [Rawlsian many-to-one matching with non-linear utility](https://mlsanity.com/paper/2511.02533/) (2025)
- [Continuous Fairness On Data Streams](https://mlsanity.com/paper/2601.08976/) (2026)

## Abstract (arXiv)

We study a novel problem of fairness in ranking aimed at minimizing the amount of individual unfairness introduced when enforcing group-fairness constraints. Our proposal is rooted in the distributional maxmin fairness theory, which uses randomization to maximize the expected satisfaction of the worst-off individuals. We devise an exact polynomial-time algorithm to find maxmin-fair distributions of general search problems (including, but not limited to, ranking), and show that our algorithm can produce rankings which, while satisfying the given group-fairness constraints, ensure that the maximum possible value is brought to individuals.