Data Feminism for AI

Abstract

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.

arXiv comment21 pages, to be published in the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24)

References in corpus (13)

• Model Cards for Model Reporting
• Gender bias and stereotypes in Large Language Models
• Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
• The Forgotten Margins of AI Ethics
• AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia
• WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
• Black Feminist Musings on Algorithmic Oppression
• Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
• The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
• Epistemic values in feature importance methods: Lessons from feminist epistemology
• Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid them
• NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs
• AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters

Cited by in corpus (5)

• African Data Ethics: A Discursive Framework for Black Decolonial Data Science
• Bridging Research and Practice Through Conversation: Reflecting on Our Experience
• Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships
• Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
• Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach