The article "Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design" by Kevin Mallinger and Ricardo Baeza-Yates explores the integration of artificial intelligence (AI) and autonomous farming machinery, highlighting the complex relationship between social, ecological, and technological dependencies. The authors emphasize the need to carefully translate technological requirements into sustainable production environments, focusing on fair data management, transparent AI approaches, and intuitive user experiences. They present a high-level framework for responsible AI development, addressing the multifaceted challenges in AI research and design. The article discusses the concept of autonomy in farming, the systemic effects of AI on farmers' autonomy, and the principles for trustworthy AI deployment. It also delves into the social, technical, and environmental aspects of AI in farming, proposing mitigation strategies such as FAIR data principles, transparent AI, and intuitive user experiences. The authors conclude with a framework for responsible AI development, emphasizing the importance of addressing social, ecological, and technological requirements to create sustainable and trustworthy AI technologies.The article "Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design" by Kevin Mallinger and Ricardo Baeza-Yates explores the integration of artificial intelligence (AI) and autonomous farming machinery, highlighting the complex relationship between social, ecological, and technological dependencies. The authors emphasize the need to carefully translate technological requirements into sustainable production environments, focusing on fair data management, transparent AI approaches, and intuitive user experiences. They present a high-level framework for responsible AI development, addressing the multifaceted challenges in AI research and design. The article discusses the concept of autonomy in farming, the systemic effects of AI on farmers' autonomy, and the principles for trustworthy AI deployment. It also delves into the social, technical, and environmental aspects of AI in farming, proposing mitigation strategies such as FAIR data principles, transparent AI, and intuitive user experiences. The authors conclude with a framework for responsible AI development, emphasizing the importance of addressing social, ecological, and technological requirements to create sustainable and trustworthy AI technologies.