Artificial intelligence for food security or digital inequality? Quantifying the global trade-offs between agricultural productivity, social inclusion, sustainable livelihoods, and responsible land stewardship

Artificial intelligence (AI) is increasingly recognized as a powerful tool for addressing some of the most pressing challenges facing global agrifood systems, including food insecurity, climate change, resource scarcity, and the growing demand for sustainable agricultural production. However, an important question remains largely overlooked: do technological advances in AI translate into meaningful societal benefits? Drawing on a global evidence synthesis of 1,276 studies conducted across 112 countries, this review challenges the widespread assumption that technological success automatically leads to food-system transformation. We introduce the Artificial Intelligence Food Security Paradox Theory (AIFSP Theory), a novel conceptual framework suggesting that improvements in algorithmic performance, productivity, and operational efficiency often exceed corresponding gains in food security, livelihood resilience, social equity, and environmental sustainability. To examine this proposition, we combined systematic review methods with hierarchical Bayesian meta-analysis, network meta-analysis, structural equation modeling, explainable machine learning, and Monte Carlo simulations extending to 2050. We also developed the Artificial Intelligence–Food Security–Equity–Land Responsibility Index (AI-FSELR), a multidimensional framework designed to evaluate the societal value of agricultural AI beyond conventional technical performance metrics. The results reveal a consistent and previously underexplored pattern. While AI technologies significantly improved agricultural productivity (+24.8%), resource-use efficiency (+18.9%), and farm income (+13.8%), gains in food security (+8.6%) and social equity (+5.4%) were considerably smaller. Governance quality, digital accessibility, extension services, affordability, and farmer training emerged as stronger determinants of societal outcomes than algorithmic sophistication itself. Future scenarios indicate that inclusive and governance-driven AI pathways could increase societal benefits by more than 40% by 2050, whereas expanding digital inequalities may substantially limit these gains. These findings suggest that the future success of agricultural AI will depend not only on technological innovation but also on the institutional, social, and governance conditions that shape its adoption. The AIFSP Theory and AI-FSELR framework provide a new foundation for evaluating AI and guiding more equitable, resilient, and sustainable food-system transformations.