mlsanityData to 14 Oct 2026

Data Feminism for AI

arXiv 2405.01286 ↗First posted 2 May 2024cs.CY cs.AIcited by 6

Not on the map. Why?

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.

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

18 of 148 references.

References · 18

  1. 2018Model Cards for Model Reportingcited by 1,080
  2. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
  3. 2023Gender bias and stereotypes in Large Language Modelscited by 187
  4. 2019Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learningcited by 116
  5. 2020Participation is not a Design Fix for Machine Learningcited by 111
  6. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  7. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  8. 2023AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asiacited by 54
  9. 2022Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selectioncited by 53
  10. 2023The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendationscited by 41
  11. 2022The Forgotten Margins of AI Ethicscited by 30
  12. 2023WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?cited by 28
6 more
  1. 2024AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filterscited by 22
  2. 2021Black Feminist Musings on Algorithmic Oppressioncited by 12
  3. 2021Epistemic values in feature importance methods: Lessons from feminist epistemologycited by 11
  4. 2023Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?cited by 6
  5. 2021Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voicescited by 2
  6. 2022Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid themcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  2. 2026Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountabilitycited by 1
  3. 2025Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionalscited by 0
  4. 2025Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approachcited by 0
  5. 2026Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costscited by 0
  6. 2026A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasmscited by 0