Predictive insights for improving the resilience of global food security using artificial intelligence

• Main Authors: HOW, Meng Leong, CHAN, Yong Jiet, CHEAH, Sin Mei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Artificial intelligence; Global food security index; Predictive modeling; Machine learning; AI for social good; Sustainability; Resilience; Bayesian; Cognitive scaffolding; Counterfactual; Agribusiness; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/6720; https://ink.library.smu.edu.sg/context/lkcsb_research/article/7719/viewcontent/sustainability_12_06272.pdf
• Institution: Singapore Management University

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, food availability, food quality and safety, and the resilience of natural resources. Exemplars are provided in this paper to illustrate how computational simulations can be used to produce forecasts of good and bad conditions in food security using multi-variant optimizations. The forecast of these future scenarios is useful for informing policy makers and stakeholders across domain verticals, so they can make decisions that are favorable to global food security.