AI and Global Food Security: A Focus on Early Warning Systems

AI and Global Food Security: A Focus on Early Warning Systems

Photo: FREDERICK FLORIN/AFP/Getty Images

Food security early warning systems, which aim to prevent instances of hunger before they take hold, are a vital mechanism for reducing the growing global hunger burden. Just as the causes of food insecurity are diverse, early warning systems for food security take a variety of forms and rely on myriad types of information. Food security early warning systems turn these data-often in the form of climatic, market, conflict, and socioeconomic variables associated with food insecurity-into predictive models that inform decisionmakers about imminent threats and recommend anticipatory actions. Given the diversity of both the drivers and indicators of food insecurity, there are many early warning systems for food security, each designed around distinct objectives, data sources, and audiences. Separately and collectively, they deliver insights with varying frequencies, focused on different geographies, all with the objective of reducing food insecurity for more people.

The Famine Early Warning System Network (FEWS NET) is one of the preeminent examples of an early warning system designed to protect food security globally. For 40 years, FEWS NET has played a central role in monitoring and forecasting acute food insecurity, thereby informing humanitarian responses by the U.S. government and other actors and supporting the development of localized early warning systems in many countries around the world. Throughout its history, FEWS NET has garnered bipartisan support for its capacity to inform decisions that ultimately reduce the incidence of hunger, and for the benefits it provides in terms of cost savings and efficiency. Nonetheless, FEWS NET, which was heavily disrupted by the dismantling of USAID in early 2025, has its own technical limitations that can result in ambiguity over the optimal time and location for decisionmakers to intervene in affected countries. Other notable examples of food security early warning systems that operate at a global scale include the Food and Agriculture Organization of the United Nations' Global Information and Early Warning System on Food and Agriculture (GIEWS) and the World Food Programme's (WFP) Vulnerability Analysis and Mapping (VAM) system.

The design of all individual early warning systems for food security reflects inherent trade-offs between scale, frequency, data availability and granularity, and analytical scope, however. Improving performance along one dimension often constrains another, shaping systems toward specific objectives across humanitarian response, risk management, and resilience building. As a result, no single system provides decisionmakers with the information and guidance needed to inform all the timely, effective anticipatory action across these objectives.

Q3: How can AI improve food security early warning systems?

A3: The effectiveness of early warning systems depends on their ability to systematically collect and analyze data related to indicators of risk and synthesize this information into actionable recommendations for decisionmakers. AI can, and does, play a significant role in facilitating improvement across all these domains.

First, AI can improve the performance of individual models by enabling them to process larger volumes of data, incorporate new data sources, and generate insights with greater frequency and/or precision. Some early warning system models have long employed machine learning (ML) techniques to process and interpret data, yet the sophistication of these models has grown significantly in recent years. Today, some food security early warning systems can synthesize greater, more diverse data sources to accomplish more predictive tasks with greater accuracy. The WFP's HungerMap LIVE, for example, has piloted a data-driven ML model in four countries using food security, agriculture, climate, conflict, social, and economic data to predict food consumption patterns over a continuous 60-day period at subnational levels. For a global early warning system fit for the purpose of the WFP's work in emergency contexts, AI integration has adapted to diverse country use cases, including by incorporating real-time VAM data.

Second, AI can facilitate improved interoperability of individual models. Whereas agricultural land cover mapping, flood detection, and marine debris monitoring are processes typically handled by individual models, NASA Harvest's Remote-sensing Agricultural Assessments for Policy Impact and Decision-making (RAAPID) program employs an AI-enabled multimodal transformer capable of completing all tasks. In this instance, AI facilitates interoperability among models using remote sensing data. Extending this operational compatibility to other systems, like HungerMap LIVE-which itself relies on a combination of food security, conflict, extreme weather, and economic data in a variety of formats-illustrates an emerging opportunity for the coalescence of diverse early warning systems for food security. Similarly, AI can help researchers both uncover and access nontraditional early warning signals that can help predict food crises earlier and more accurately. ZeroHungerAI at the World Bank, for example, is leveraging the predictive power of news media, engaging large language models to convert data collected from local news sources into high-frequency indicators of food insecurity. These signals can then be used to increase the robustness of existing early warning systems in relevant regions.

Third, to this end, AI can reduce barriers to entry for technical partners and create more opportunities for collaboration within the early warning system community and its stakeholders. The University of California, Berkeley, for example, has led an initiative with Kenya's National Drought Management Authority (NDMA) to develop an ML-based tool internal to the Kenyan government that generates accurate forecasts of acute malnutrition and integrates them into an early warning dashboard. The model was codesigned with key stakeholders and decisionmakers, fully integrating the NDMA's existing data sources and improving model capability with data on indicators like rainfall and vegetation health. The project exemplifies the opportunities AI presents to stimulate collaboration among various technical organizations and stakeholders, in this instance including UC Santa Barbara, the Jameel Observatory, Cornell University, and Google in addition to UC Berkeley and the NDMA. Alternatively, the increasing availability of novel AI modeling developed independently of food security early warning systems is an emergent boon for the food security community. Resources like the Google NeuralGCM, which is a component of Earth AI and simulates long-range precipitation predictions, can become invaluable tools for researchers who rely on weather predictions for their early warning systems-pushing back the lead time for anticipatory action.

Q4: What are the limitations to and risks of AI-integrated early warning systems for food security?

A4: The risks and limitations of AI usage for food security early warning systems mirror those that already exist for early warning systems of all kinds, as well as those that exist for AI-informed decisionmaking. The ability of an early warning system to produce informed action is dependent on several factors, including trust in both the AI models and human oversight structure used, data literacy among decisionmakers, and common standards for the guidance delivered by early warning systems. Without clear communication regarding the assumptions and limitations inherent to the insights provided by these early warning models, AI-enabled forecasts risk being misunderstood or misapplied by decisionmakers, reducing trust in the AI-enabled models themselves. AI does not resolve these challenges; rather, it can heighten consequences and complicate responsibility, particularly where trust is fragile or governance structures are weak.

As such, a central risk associated with integrating AI into preexisting food security early warning systems is the potential for an erosion of confidence across advisory organizations, governments, local actors, and affected communities. The potential for such an outcome is top of mind for those developing these AI tools and models, as a primary shared goal is to pool, collate, and harmonize their high-quality and highly localized data collections. Biased or incomplete data inputs can impart amplified harm through AI-driven analysis, with these models able to scale both positive and adverse outcomes farther and faster. The risks associated with low-quality data can be mitigated with human oversight of the model and validation by local experts.

Q5: What U.S. and global policies are needed?

A5: In the context of dominant political narratives regarding the race for global AI leadership, the work of identifying practical applications of AI for human well-being continues. As AI-enabled tools for data collection and analysis become more accessible, policymakers and technical experts must consider what is needed today to mitigate risks associated with low model transparency, biased data, and uneven institutional capacity in the future. There remains an urgent need for public and private sectors to create conditions for the development and sharing of best practices and harmonized approaches.

Policies that enable open data exchange are a critical first step. The United States' Genesis Mission illustrates the role that national governments may choose to play in accelerating AI research and data access. While outcomes for foundational and applied AI research remain to be seen, it emphasizes the identification of data gaps, improvements in anonymization and privacy-preserving techniques, and the development of shared metadata, documentation, and provenance standards-all essential elements of an open information exchange. This type of access to scientific and proprietary data is especially critical for accurately modeling the vast range of variables that cause conditions for food insecurity to emerge or worsen.

Furthermore, policy should support structured experimentation and institutional learning. Concentrated effort by governments, multilateral organizations, and nongovernmental organizations is needed to pilot AI-enabled early warning applications that synthesize insights across systems and contexts. Such efforts could identify user needs, explore avenues to increase transparency, and uncover opportunities for greater technical and institutional cooperation. Equally essential is a sustained investment in building out networks of local experts capable of validating data inputs, interpreting model outcomes, and supporting the creation of educational materials for decisionmakers. It is critical that local experts are engaged alongside technical experts to maintain oversight of a model's guidance and corresponding implementation.

To that end, policy should encourage the development of shared standards for human oversight of AI-enabled models so that responsibilities for interpretation, validation, and action are well defined within existing institutional decisionmaking structures. This is especially critical in food security contexts, where local political dynamics, cultural factors, and informal markets shape outcomes in ways that may not be captured in data. By anchoring AI-enabled early warning system implementation to accountable human judgment-supported by local expertise and transparent governance-policy can help reduce the risk of misguided implementation and strengthen trust between decisionmakers and affected communities.

Ultimately, policy that encourages the production of effective food security early warning systems reinforces a clear principle: AI is a tool, not a solution in and of itself. AI-enabled techniques portend yet unseen benefits for data collection, analysis, cooperation, communication, and better informed, anticipatory action. Still, early warning systems alone-even expert-validated AI-enabled insights-are not enough to induce action among decisionmakers. The threshold for a decisionmaker to act upon the recommendations of early warning systems is high, and even "perfect" data and accurate forecasts may not be enough to overcome political priorities, resource limitations, and human barriers to aid delivery. However, as hunger crises continue to emerge around the world, U.S. and global leadership will do well to remember that an ounce of prevention is worth a pound of cure.

Emma Dodd is a research associate with the Global Food and Water Security Program at the Center for Strategic and International Studies (CSIS) in Washington, D.C. Zane Swanson is the deputy director of the Global Food and Water Security Program at CSIS. Caitlin Welsh is the director of the Global Food and Water Security Program at CSIS.

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