This article discusses the integration of artificial intelligence (AI) in farming and presents a multi-criteria framework for responsible AI development. The authors highlight the challenges and opportunities of AI in farming, emphasizing the need for responsible technology design that considers social, ecological, and technological dependencies. The article outlines the concept of autonomy in farming and the socio-technological-ecological system (STES) approach, which is crucial for understanding the complex interactions between AI and farming practices. It also addresses the current challenges of AI in maintaining farmers' autonomy, including social, technical, and environmental factors. The article proposes mitigation strategies through technological developments, such as the FAIR data principles, transparent AI, and intuitive user experiences. These strategies aim to ensure responsible AI development that supports sustainable and equitable farming practices. The authors also present a framework for AI development that considers the social, technological, and ecological implications of system design requirements. The framework emphasizes the need for symmetric attention to these factors and provides guidance for creating responsible AI technologies. The article concludes with a discussion on future work, including the integration of fairness indicators and the use of digital twins to validate AI models. Overall, the article advocates for a holistic approach to AI development in farming that prioritizes sustainability, fairness, and the well-being of farmers.This article discusses the integration of artificial intelligence (AI) in farming and presents a multi-criteria framework for responsible AI development. The authors highlight the challenges and opportunities of AI in farming, emphasizing the need for responsible technology design that considers social, ecological, and technological dependencies. The article outlines the concept of autonomy in farming and the socio-technological-ecological system (STES) approach, which is crucial for understanding the complex interactions between AI and farming practices. It also addresses the current challenges of AI in maintaining farmers' autonomy, including social, technical, and environmental factors. The article proposes mitigation strategies through technological developments, such as the FAIR data principles, transparent AI, and intuitive user experiences. These strategies aim to ensure responsible AI development that supports sustainable and equitable farming practices. The authors also present a framework for AI development that considers the social, technological, and ecological implications of system design requirements. The framework emphasizes the need for symmetric attention to these factors and provides guidance for creating responsible AI technologies. The article concludes with a discussion on future work, including the integration of fairness indicators and the use of digital twins to validate AI models. Overall, the article advocates for a holistic approach to AI development in farming that prioritizes sustainability, fairness, and the well-being of farmers.