Abstract
Abstract: 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 considerations, followed by policy recommendations in crucial areas such as funding, cross-sector collaboration, education, and data standards. Finally, it underscores the importance of recognizing AI as a complementary tool, rather than a standalone solution, in the pursuit of the ultimate goal of achieving a world without hunger.
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