# Fairness in Ranking: A Survey

arXiv: https://arxiv.org/abs/2103.14000 · HTML: https://mlsanity.com/paper/2103.14000/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2021.

## Built on by (most cited first, 30 of 33)

- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [FRAPPE: A Group Fairness Framework for Post-Processing Everything](https://mlsanity.com/paper/2312.02592/) (2023)
- [Identifying Biased Subgroups in Ranking and Classification](https://mlsanity.com/paper/2108.07450/) (2021)
- [Debiasing Neural Retrieval via In-batch Balancing Regularization](https://mlsanity.com/paper/2205.09240/) (2022)
- [Language Fairness in Multilingual Information Retrieval](https://mlsanity.com/paper/2405.00978/) (2024)
- [Towards Standardizing AI Bias Exploration](https://mlsanity.com/paper/2405.19022/) (2024)
- [Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking](https://mlsanity.com/paper/2204.13765/) (2022)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [Fairness in Ranking under Disparate Uncertainty](https://mlsanity.com/paper/2309.01610/) (2023)
- [Stability and Multigroup Fairness in Ranking with Uncertain Predictions](https://mlsanity.com/paper/2402.09326/) (2024)
- [A Pre-processing Method for Fairness in Ranking](https://mlsanity.com/paper/2110.15503/) (2021)
- [The Role of Relevance in Fair Ranking](https://mlsanity.com/paper/2305.05608/) (2023)
- [Evaluative Item-Contrastive Explanations in Rankings](https://mlsanity.com/paper/2312.10094/) (2023)
- [On Explaining Unfairness: An Overview](https://mlsanity.com/paper/2402.10762/) (2024)
- [Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation](https://mlsanity.com/paper/2503.13463/) (2025)
- [Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities](https://mlsanity.com/paper/2112.08268/) (2021)
- [Contextual bandits with concave rewards, and an application to fair ranking](https://mlsanity.com/paper/2210.09957/) (2022)
- [Fairness Through Counterfactual Utilities](https://mlsanity.com/paper/2108.05315/) (2021)
- [Fair Decision-Making for Food Inspections](https://mlsanity.com/paper/2108.05523/) (2021)
- [Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank](https://mlsanity.com/paper/2111.00735/) (2021)
- [Fairness-aware Differentially Private Collaborative Filtering](https://mlsanity.com/paper/2303.09527/) (2023)
- [Software Doping Analysis for Human Oversight](https://mlsanity.com/paper/2308.06186/) (2023)
- [Explaining Search Result Stances to Opinionated People](https://mlsanity.com/paper/2309.08460/) (2023)
- [FROC: Building Fair ROC from a Trained Classifier](https://mlsanity.com/paper/2412.14724/) (2024)
- [What's in a Query: Polarity-Aware Distribution-Based Fair Ranking](https://mlsanity.com/paper/2502.11429/) (2025)
- [Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias](https://mlsanity.com/paper/2603.24218/) (2026)

## Abstract (arXiv)

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. In this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. Then, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods.