# A Tutorial On Intersectionality in Fair Rankings

arXiv: https://arxiv.org/abs/2502.05333 · HTML: https://mlsanity.com/paper/2502.05333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 70 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [Designing Fair Ranking Schemes](https://mlsanity.com/paper/1712.09752/) (2017)
- [Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey](https://mlsanity.com/paper/2309.13933/) (2023)
- [Mitigating Bias in Set Selection with Noisy Protected Attributes](https://mlsanity.com/paper/2011.04219/) (2020)
- [Interventions for Ranking in the Presence of Implicit Bias](https://mlsanity.com/paper/2001.08767/) (2020)
- [User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets](https://mlsanity.com/paper/2010.01470/) (2020)
- [When Fair Ranking Meets Uncertain Inference](https://mlsanity.com/paper/2105.02091/) (2021)
- [Balanced Ranking with Diversity Constraints](https://mlsanity.com/paper/1906.01747/) (2019)
- [De-biasing "bias" measurement](https://mlsanity.com/paper/2205.05770/) (2022)
- [InfoFair: Information-Theoretic Intersectional Fairness](https://mlsanity.com/paper/2105.11069/) (2021)
- [Causal Multi-Level Fairness](https://mlsanity.com/paper/2010.07343/) (2020)
- [Causal intersectionality for fair ranking](https://mlsanity.com/paper/2006.08688/) (2020)
- [FairCanary: Rapid Continuous Explainable Fairness](https://mlsanity.com/paper/2106.07057/) (2021)
- [A Nutritional Label for Rankings](https://mlsanity.com/paper/1804.07890/) (2018)
- [On Measuring Bias in Online Information](https://mlsanity.com/paper/1704.05730/) (2017)
- [The Schulze Method of Voting](https://mlsanity.com/paper/1804.02973/) (2018)
- [On Obtaining Stable Rankings](https://mlsanity.com/paper/1804.10990/) (2018)
- [Teaching Responsible Data Science: Charting New Pedagogical Territory](https://mlsanity.com/paper/1912.10564/) (2019)
- [The Role of Relevance in Fair Ranking](https://mlsanity.com/paper/2305.05608/) (2023)
- [MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking](https://mlsanity.com/paper/2207.10020/) (2022)

## Abstract (arXiv)

We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a data-driven world, especially against marginalized and underrepresented groups. Efforts towards responsible data science and responsible artificial intelligence aim to mitigate these biases and promote fairness, diversity, and transparency. However, most fairness-aware ranking methods singularly focus on protected attributes such as race, gender, or socio-economic status, neglecting the intersectionality of these attributes, i.e., the interplay between multiple social identities. Understanding intersectionality is crucial to ensure that existing inequalities are not preserved by fair rankings. We offer a description of the main ways to incorporate intersectionality in fair ranking systems through practical examples and provide a comparative overview of existing literature and a synoptic table summarizing the various methodologies. Our analysis highlights the need for intersectionality to attain fairness, while also emphasizing that fairness, alone, does not necessarily imply intersectionality.