Data Feminism for AI
FAccT ’24, June 03–06, 2024, Rio de Janeiro, Brazil
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency
2024
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| Online Access: | https://hdl.handle.net/1721.1/155777 |
| _version_ | 1824458021201248256 |
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| author | Klein, Lauren D'Ignazio, Catherine |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Klein, Lauren D'Ignazio, Catherine |
| author_sort | Klein, Lauren |
| collection | MIT |
| description | FAccT ’24, June 03–06, 2024, Rio de Janeiro, Brazil |
| first_indexed | 2024-09-23T10:26:31Z |
| format | Article |
| id | mit-1721.1/155777 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:19:16Z |
| publishDate | 2024 |
| publisher | ACM|FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency |
| record_format | dspace |
| spelling | mit-1721.1/1557772024-12-23T04:57:03Z Data Feminism for AI Klein, Lauren D'Ignazio, Catherine Massachusetts Institute of Technology. Department of Urban Studies and Planning FAccT ’24, June 03–06, 2024, Rio de Janeiro, Brazil 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. 2024-07-24T16:12:57Z 2024-07-24T16:12:57Z 2024-06-03 2024-07-01T07:55:17Z Article http://purl.org/eprint/type/ConferencePaper 979-8-4007-0450-5 https://hdl.handle.net/1721.1/155777 Klein, Lauren and D'Ignazio, Catherine. 2024. "Data Feminism for AI." PUBLISHER_CC en 10.1145/3630106.3658543 Creative Commons Attribution-Noncommercial-ShareAlike https://creativecommons.org/licenses/by-nc-sa/4.0/ The author(s) application/pdf ACM|FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency Association for Computing Machinery |
| spellingShingle | Klein, Lauren D'Ignazio, Catherine Data Feminism for AI |
| title | Data Feminism for AI |
| title_full | Data Feminism for AI |
| title_fullStr | Data Feminism for AI |
| title_full_unstemmed | Data Feminism for AI |
| title_short | Data Feminism for AI |
| title_sort | data feminism for ai |
| url | https://hdl.handle.net/1721.1/155777 |
| work_keys_str_mv | AT kleinlauren datafeminismforai AT dignaziocatherine datafeminismforai |