Responsible Artificial Intelligence in Agriculture: A Systematic Literature Review of Ethical Challenges and Required Characteristics

With AI (Artificial Intelligence) and ML (Machine Learning) becoming part of present-day agriculture, including crop-position monitoring and autonomous devices, the concept of Responsible AI (RAI) has ceased to be merely a hypothetical argument and has become a management-level requirement. The paper is a systematic literature review (SLR) on the ethical frontier of AI in the agricultural industry. After a thorough methodology, the main ethical issues (e.g., data privacy, algorithmic bias, and accountability) are identified and classified, and the needed RAI properties (e.g., explainability, fairness, and robustness) are identified to be implemented sustainably. Mapping these findings into four major sub-sectors, namely Precision Crop Farming, Livestock Management, Supply Chain, and Autonomous Machinery, this study offers a strategic framework to researchers and agricultural managers to make sure that technological innovation is in line with the values of society and ethics.