# Data Feminism for AI

arXiv: https://arxiv.org/abs/2405.01286 · HTML: https://mlsanity.com/paper/2405.01286/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 148 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 18 of 18)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection](https://mlsanity.com/paper/2201.10474/) (2022)
- [The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations](https://mlsanity.com/paper/2308.02053/) (2023)
- [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022)
- [WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?](https://mlsanity.com/paper/2305.06415/) (2023)
- [AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://mlsanity.com/paper/2401.06408/) (2024)
- [Black Feminist Musings on Algorithmic Oppression](https://mlsanity.com/paper/2101.09869/) (2021)
- [Epistemic values in feature importance methods: Lessons from feminist epistemology](https://mlsanity.com/paper/2101.12737/) (2021)
- [Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?](https://mlsanity.com/paper/2303.07242/) (2023)
- [Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voices](https://mlsanity.com/paper/2101.07463/) (2021)
- [Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid them](https://mlsanity.com/paper/2203.08227/) (2022)

## Built on by (most cited first, 6 of 6)

- [Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships](https://mlsanity.com/paper/2407.12543/) (2024)
- [Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability](https://mlsanity.com/paper/2604.16106/) (2026)
- [Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://mlsanity.com/paper/2504.00860/) (2025)
- [Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach](https://mlsanity.com/paper/2508.17944/) (2025)
- [Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs](https://mlsanity.com/paper/2603.27376/) (2026)
- [A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms](https://mlsanity.com/paper/2607.15480/) (2026)

## Abstract (arXiv)

This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. In Data Feminism (2020), we offered seven principles for examining and challenging unequal power in data science. Here, we present a rationale for why feminism remains deeply relevant for AI research, rearticulate the original principles of data feminism with respect to AI, and introduce two potential new principles related to environmental impact and consent. Together, these principles help to 1) account for the unequal, undemocratic, extractive, and exclusionary forces at work in AI research, development, and deployment; 2) identify and mitigate predictable harms in advance of unsafe, discriminatory, or otherwise oppressive systems being released into the world; and 3) inspire creative, joyful, and collective ways to work towards a more equitable, sustainable world in which all of us can thrive.