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.

Sharon Hogge, an electronics engineer, poses with autonomous sentry robot ROBART I and the HT3 Industrial Robot. Technology surrounds us and is everywhere, but how this technology is made and who really benefits from it and who does not, are still important questions to be investigated. Recently, movements have emerged such as Data Feminism and Design Justice, which analyse technology from a more critical angle with the intention of creating more equity in technological practice. We explore some of their implications based on the artistic project Feminist Data Set, hand in hand with its creator, who explores what an intersectional feminist machine learning labelling and training system would be like and what would be necessary to construct it. In a time of the re-rising of fascism, of what feels like a lessening of social justice values, and in an age of global digitization, social justice has never been more integral in the space of interrogating data, technology, and the structure of society itself.

Our ability to collect and record information in a digital form has exploded as has our adoption of AI systems, which use data to make decisions. But data isn't neutral, and sexism, racism and other forms of discrimination are showing up in our data products. Catherine D'Ignazio, an assistant professor of urban science and planning at the Massachusetts Institute of Technology (MIT), argues we need to do better. Along with Lauren Klein, who directs the Digital Humanities Lab at Emory University, she is the co-author of the new book Data Feminism, which charts a course for a more equitable data science. D'Ignazio also directs MIT's new Data and Feminism lab, which seeks to use data and computation to counter oppression.

Suppose you would like to know mortality rates for women during childbirth, by country, around the world. One option is the WomanStats Project, the website of an academic research effort investigating the links between the security and activities of nation-states, and the security of the women who live in them. The project, founded in 2001, meets a need by patching together data from around the world. Many countries are indifferent to collecting statistics about women's lives. But even where countries try harder to gather data, there are clear challenges to arriving at useful numbers -- whether it comes to women's physical security, property rights, and government participation, among many other issues.