Data Feminism for AI

Lauren Klein, Catherine D'Ignazio
5/2/2024
cs.CYcs.AI

No confident code implementation found for this paper. 4 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 4 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach

83% match2025

QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processes

81% match2025

(Unfair) Norms in Fairness Research: A Meta-Analysis

77% match2024

Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness

77% match2025

"My Kind of Woman": Analysing Gender Stereotypes in AI through The Averageness Theory and EU Law

76% match2024

Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness

76% match2023

Cite this paper

@article{klein2024data,
  title  = {Data Feminism for AI},
  author = {Lauren Klein and Catherine D'Ignazio},
  year   = {2024},
  eprint = {2405.01286},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.01286v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.01286.svg)](https://codeofpaper.com/paper/2405.01286)

Discussion