Lauren Klein and Catherine D'Ignazio present data feminism as a framework for equitable, ethical, and sustainable AI research. The paper rearticulates the original seven data feminism principles for AI, introduces two new principles on environmental impact and consent, and highlights the need to address the unequal, undemocratic, extractive, and exclusionary forces in AI research, development, and deployment. It emphasizes the importance of identifying and mitigating predictable harms in advance of unsafe, discriminatory, or oppressive systems being released into the world, and inspiring creative, joyful, and collective ways to work towards a more equitable, sustainable world. The paper also discusses the relevance of feminism in AI research, drawing on intersectional feminism, which challenges structural inequalities and highlights the importance of considering the lived experiences of marginalized groups. The paper critiques the current state of AI research, pointing out the dominance of cisgender men and the exclusion of women, trans, and nonbinary people, as well as people of color, in AI research and development. It calls for a more inclusive, diverse, and ethical approach to AI research, emphasizing the need for participatory design, transparency, and accountability. The paper also highlights the importance of considering the historical and social contexts in which AI systems are developed and deployed, and the need to challenge the power imbalances that underpin AI research and development. The paper concludes by calling for a more human-centered, ethical, and sustainable approach to AI research, one that prioritizes the well-being of all people and the planet.Lauren Klein and Catherine D'Ignazio present data feminism as a framework for equitable, ethical, and sustainable AI research. The paper rearticulates the original seven data feminism principles for AI, introduces two new principles on environmental impact and consent, and highlights the need to address the unequal, undemocratic, extractive, and exclusionary forces in AI research, development, and deployment. It emphasizes the importance of identifying and mitigating predictable harms in advance of unsafe, discriminatory, or oppressive systems being released into the world, and inspiring creative, joyful, and collective ways to work towards a more equitable, sustainable world. The paper also discusses the relevance of feminism in AI research, drawing on intersectional feminism, which challenges structural inequalities and highlights the importance of considering the lived experiences of marginalized groups. The paper critiques the current state of AI research, pointing out the dominance of cisgender men and the exclusion of women, trans, and nonbinary people, as well as people of color, in AI research and development. It calls for a more inclusive, diverse, and ethical approach to AI research, emphasizing the need for participatory design, transparency, and accountability. The paper also highlights the importance of considering the historical and social contexts in which AI systems are developed and deployed, and the need to challenge the power imbalances that underpin AI research and development. The paper concludes by calling for a more human-centered, ethical, and sustainable approach to AI research, one that prioritizes the well-being of all people and the planet.