Data Feminism for AI
Name
3630106.3658543.pdf
Size
600.16 KB
Format
Adobe PDF
Checksum (MD5)
6580875dd2129dfd20e34123a29a4eda
Author(s)
Klein, Lauren
D'Ignazio, Catherine
Date Issued
June 3, 2024
Publisher
ACM|FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency
Citation
Klein, Lauren and D'Ignazio, Catherine. 2024. "Data Feminism for AI."
Version
Final published version
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.
Description
FAccT ’24, June 03–06, 2024, Rio de Janeiro, Brazil
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3630106.3658543
