Data Feminism for AI

TL;DR

This paper reframes Data Feminism for AI by arguing that power, inequality, and extractive practices remain central to AI research, development, and deployment. It rearticulates the seven original data-feminist principles in AI contexts and adds two new principles addressing environmental impact and consent, aiming to mitigate harms before they materialize. The authors draw on feminist theory, FAccT scholarship, and concrete examples (e.g., counterdata, data trusts, feminicide classifiers, and environment/consent considerations) to show how a feminist governance framework can guide equitable, participatory, and anti-extractive AI. The work advances a practical, justice-oriented template for AI research and policy that foregrounds power analysis, pluralism, context, and labor to foster sustainable, inclusive technological futures.

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.