This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. The authors, Lauren Klein and Catherine D'Ignazio, argue that feminism remains deeply relevant for AI research and rearticulate the original principles of data feminism with respect to AI. They introduce two new principles related to environmental impact and consent. These principles aim to address the unequal, undemocratic, extractive, and exclusionary forces in AI research, development, and deployment, identify and mitigate predictable harms, and inspire creative, joyful, and collective ways to work towards a more equitable, sustainable world. The paper begins by explaining the relevance of feminism in AI research, highlighting the power dynamics and structural inequalities in data science. It then discusses the background of feminism, emphasizing the importance of intersectional feminism in addressing multiple forms of social difference and structural power. The authors review the seven principles of data feminism and explain how they can be adapted to AI research, focusing on examining and challenging power, rethinking binaries and hierarchies, elevating emotion and embodiment, embracing pluralism, and considering context. The paper also addresses the need for collective visioning and action, government regulation, and policy changes to counter corporate capture in AI. It emphasizes the importance of participatory and democratic approaches in AI development and the need for more inclusive and ethical AI research practices. The authors provide examples of successful initiatives and ongoing efforts to apply these principles in AI research and development.This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. The authors, Lauren Klein and Catherine D'Ignazio, argue that feminism remains deeply relevant for AI research and rearticulate the original principles of data feminism with respect to AI. They introduce two new principles related to environmental impact and consent. These principles aim to address the unequal, undemocratic, extractive, and exclusionary forces in AI research, development, and deployment, identify and mitigate predictable harms, and inspire creative, joyful, and collective ways to work towards a more equitable, sustainable world. The paper begins by explaining the relevance of feminism in AI research, highlighting the power dynamics and structural inequalities in data science. It then discusses the background of feminism, emphasizing the importance of intersectional feminism in addressing multiple forms of social difference and structural power. The authors review the seven principles of data feminism and explain how they can be adapted to AI research, focusing on examining and challenging power, rethinking binaries and hierarchies, elevating emotion and embodiment, embracing pluralism, and considering context. The paper also addresses the need for collective visioning and action, government regulation, and policy changes to counter corporate capture in AI. It emphasizes the importance of participatory and democratic approaches in AI development and the need for more inclusive and ethical AI research practices. The authors provide examples of successful initiatives and ongoing efforts to apply these principles in AI research and development.