# Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness

arXiv: https://arxiv.org/abs/2303.17555 · HTML: https://mlsanity.com/paper/2303.17555/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 98 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 29 of 29)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models](https://mlsanity.com/paper/2102.04130/) (2021)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases](https://mlsanity.com/paper/2010.15052/) (2020)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [Characterizing Intersectional Group Fairness with Worst-Case Comparisons](https://mlsanity.com/paper/2101.01673/) (2021)
- [Disembodied Machine Learning: On the Illusion of Objectivity in NLP](https://mlsanity.com/paper/2101.11974/) (2021)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research](https://mlsanity.com/paper/2102.01203/) (2021)
- [Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness](https://mlsanity.com/paper/2010.05434/) (2020)
- [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022)
- [Bayesian Modeling of Intersectional Fairness: The Variance of Bias](https://mlsanity.com/paper/1811.07255/) (2018)
- [Intersectional Bias in Hate Speech and Abusive Language Datasets](https://mlsanity.com/paper/2005.05921/) (2020)
- [Towards decolonising computational sciences](https://mlsanity.com/paper/2009.14258/) (2020)
- [InfoFair: Information-Theoretic Intersectional Fairness](https://mlsanity.com/paper/2105.11069/) (2021)
- [Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic](https://mlsanity.com/paper/2204.03558/) (2022)
- [Causal intersectionality for fair ranking](https://mlsanity.com/paper/2006.08688/) (2020)
- [Black Feminist Musings on Algorithmic Oppression](https://mlsanity.com/paper/2101.09869/) (2021)
- [Algorithmic Injustices: Towards a Relational Ethics](https://mlsanity.com/paper/1912.07376/) (2019)
- [Bounding and Approximating Intersectional Fairness through Marginal Fairness](https://mlsanity.com/paper/2206.05828/) (2022)
- [Making Intelligence: Ethical Values in IQ and ML Benchmarks](https://mlsanity.com/paper/2209.00692/) (2022)
- [Fairness Implications of Encoding Protected Categorical Attributes](https://mlsanity.com/paper/2201.11358/) (2022)
- [Intersectionality: Multiple Group Fairness in Expectation Constraints](https://mlsanity.com/paper/1811.09960/) (2018)
- [One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification](https://mlsanity.com/paper/2010.13494/) (2020)
- [Algorithmic Bias in Machine Learning Based Delirium Prediction](https://mlsanity.com/paper/2211.04442/) (2022)

## Built on by (most cited first, 26 of 26)

- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation](https://mlsanity.com/paper/2404.01030/) (2024)
- [TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models](https://mlsanity.com/paper/2312.01261/) (2023)
- [Building Socio-culturally Inclusive Stereotype Resources with Community Engagement](https://mlsanity.com/paper/2307.10514/) (2023)
- [Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP](https://mlsanity.com/paper/2405.17159/) (2024)
- [LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions](https://mlsanity.com/paper/2406.08824/) (2024)
- [When is Multicalibration Post-Processing Necessary?](https://mlsanity.com/paper/2406.06487/) (2024)
- [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023)
- [Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction](https://mlsanity.com/paper/2309.17417/) (2023)
- [Taxonomizing Representational Harms using Speech Act Theory](https://mlsanity.com/paper/2504.00928/) (2025)
- [Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs](https://mlsanity.com/paper/2505.02456/) (2025)
- [Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness](https://mlsanity.com/paper/2409.10825/) (2024)
- [Laissez-Faire Harms: Algorithmic Biases in Generative Language Models](https://mlsanity.com/paper/2404.07475/) (2024)
- [Learning With Multi-Group Guarantees For Clusterable Subpopulations](https://mlsanity.com/paper/2410.14588/) (2024)
- [Multi-Group Proportional Representation for Text-to-Image Models](https://mlsanity.com/paper/2505.24023/) (2025)
- [Research Borderlands: Analysing Writing Across Research Cultures](https://mlsanity.com/paper/2506.00784/) (2025)
- [Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution](https://mlsanity.com/paper/2508.07111/) (2025)
- [The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes](https://mlsanity.com/paper/2405.20582/) (2024)
- [Data Defenses Against Large Language Models](https://mlsanity.com/paper/2410.13138/) (2024)
- [Efficient Model-Agnostic Multi-Group Equivariant Networks](https://mlsanity.com/paper/2310.09675/) (2023)
- [Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models](https://mlsanity.com/paper/2407.06917/) (2024)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languages](https://mlsanity.com/paper/2501.13836/) (2025)
- [Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach](https://mlsanity.com/paper/2508.17944/) (2025)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)

## Abstract (arXiv)

Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine how social inequalities persist through domains of structure and discipline. Given AI fairness' raison d'etre of "fairness", we argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness. Through a critical review of how intersectionality is discussed in 30 papers from the AI fairness literature, we deductively and inductively: 1) map how intersectionality tenets operate within the AI fairness paradigm and 2) uncover gaps between the conceptualization and operationalization of intersectionality. We find that researchers overwhelmingly reduce intersectionality to optimizing for fairness metrics over demographic subgroups. They also fail to discuss their social context and when mentioning power, they mostly situate it only within the AI pipeline. We: 3) outline and assess the implications of these gaps for critical inquiry and praxis, and 4) provide actionable recommendations for AI fairness researchers to engage with intersectionality in their work by grounding it in AI epistemology.