Data Feminism for AI

2024/05/02 by Lauren Klein, Catherine D'Ignazio, Catherine D’Ignazio · 1 voice · 12 citations
Social Sciences · Neuroscience · #Ethics and Social Impacts of AI #Neuroethics, Human Enhancement, Biomedical Innovations

paper · pdf · doi:10.1145/3630106.3658543

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.

Citations

• On the Dangers of Stochastic Parrots
• Gender bias and stereotypes in Large Language Models

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• Transphobia Is in the Eye of the Prompter: Trans-Centered Perspectives on Large Language Models
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Discussions

• More new work! "Data Feminism for AI," with @kanarinka.bsky.social, which reflects our current thinking about how and why data feminism remains deeply relevant for AI research: arxiv.org/abs/2405.012 [bsky, 41 points, 1 comments]

Related

• Data Feminism
• Data Ecofeminism
• Queering the ethics of AI
• Design Heuristics for Emerging Technologies: AI, Data, & Human-Centered Futures – Considerations for the Rights of Women
• QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processes