Artificial intelligence and food insecurity: opportunities, risks, and equity pathways for the Global South

Food insecurity affects 691–783 million people globally, with disproportionate impacts on low-income and middle-income countries, marginalized groups, and conflict-affected populations. Artificial intelligence (AI) presents unprecedented opportunities to address food security challenges through precision agriculture, supply chain optimization, and early warning systems, achieving up to 91% accuracy in crop yield prediction and reducing food waste by 22%. Its deployment risks perpetuating existing inequities. Drawing on a systematic literature search of 64 peer-reviewed studies across Scopus, Web of Science, PubMed, AGRIS, and IEEE Xplore (2015–2025), this review examines AI’s double-edged role in food systems through a comprehensive framework encompassing inclusive data practices, equitable governance, participatory design, and local capacity building. Key risks include data colonialism, algorithmic bias favoring industrial agriculture, the digital exclusion of vulnerable communities, and technological dependency that threatens food sovereignty. However, evidence from successful implementations in sub-Saharan Africa demonstrates that inclusive approaches can enhance productivity while promoting gender equality and environmental sustainability. The study concludes that AI’s impact of AI fundamentally depends on the design principles and governance frameworks that prioritize equity. Without inclusive implementation strategies, AI may reinforce hunger gaps; conversely, equity-centered approaches can contribute to resilient, just, and food-secure systems for all populations.