Food security and AI: From data collection to early warning systems

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Food security and AI: From data collection to early warning systems

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  • Characterizing the socio-economic status of populations and providing reliable, up-to-date estimates of who the most vulnerable are, how many of them there are, where they reside, and why they are vulnerable, is essential for governments and humanitarian organizations to make informed and timely decisions regarding the implementation of humanitarian assistance policies and programs. Anticipatory and preventive actions have been demonstrated to yield significant benefits compared to post-disaster and post-shock aid. It is, therefore, crucial to understand the drivers of food insecurity in a context where extreme weather events play an increasingly significant role in exacerbating already critical situations. The work of WFP’s Hunger Monitoring Unit revolves around collecting reliable food security data, which is then analyzed in conjunction with climate, economic, and conflict data to support early warning systems, including the use of machine learning.


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