# The Intersectionality Problem for Algorithmic Fairness

arXiv: https://arxiv.org/abs/2411.02569 · HTML: https://mlsanity.com/paper/2411.02569/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks](https://mlsanity.com/paper/2106.05498/) (2021)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [Auditing and Achieving Intersectional Fairness in Classification Problems](https://mlsanity.com/paper/1911.01468/) (2019)
- [Bayesian Modeling of Intersectional Fairness: The Variance of Bias](https://mlsanity.com/paper/1811.07255/) (2018)
- [De-biasing "bias" measurement](https://mlsanity.com/paper/2205.05770/) (2022)
- [Statistical Inference for Fairness Auditing](https://mlsanity.com/paper/2305.03712/) (2023)
- [Bounding and Approximating Intersectional Fairness through Marginal Fairness](https://mlsanity.com/paper/2206.05828/) (2022)
- [Debiasing classifiers: is reality at variance with expectation?](https://mlsanity.com/paper/2011.02407/) (2020)
- [Lale: Consistent Automated Machine Learning](https://mlsanity.com/paper/2007.01977/) (2020)
- [A Suite of Fairness Datasets for Tabular Classification](https://mlsanity.com/paper/2308.00133/) (2023)
- [An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models](https://mlsanity.com/paper/2302.04525/) (2023)

## Built on by (most cited first, 5 of 5)

- [Size-adaptive Hypothesis Testing for Fairness](https://mlsanity.com/paper/2506.10586/) (2025)
- [MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups](https://mlsanity.com/paper/2603.13452/) (2026)
- [Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach](https://mlsanity.com/paper/2508.17944/) (2025)
- [Auditability and the Landscape of Distance to Multicalibration](https://mlsanity.com/paper/2509.16930/) (2025)
- [Position: Fairness Failure in Generative Models is an Evaluation Problem](https://mlsanity.com/paper/2608.16974/) (2026)

## Abstract (arXiv)

A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges. This paper (1) elucidates the problem of intersectionality in algorithmic fairness, (2) develops desiderata to clarify the challenges underlying the problem and guide the search for potential solutions, (3) illustrates the desiderata and potential solutions by sketching a proposal using simple hypothesis testing, and (4) evaluates, partly empirically, this proposal against the proposed desiderata.