Using Artificial Intelligence to Predict and Prevent Future Food Insecurity

The article explores the role and prospects of artificial intelligence (AI) in addressing global food insecurity. It provides an overview of machine learning (ML) techniques—the core learning component of AI—used to predict food security outcomes and discusses real-world examples as well as recent applications of ML. It further examines the challenges and limitations of ML, including concerns related to data quality and ethical con-siderations, followed by policy recommendations in crucial areas such as funding, cross-sector collabo-ration, education, and data standards. Finally, it underscores the importance of recognizing AI as a complementary tool, rather than a standalone solu-tion, in the pursuit of the ultimate goal of achieving a world without hunger.