How Machine Learning Can Improve Food Insecurity Predictions

By University of Illinois | College of ACES

Food insecurity in low-income countries is on the rise as climate variation and economic shocks, including the COVID-19 pandemic, take their toll, writes Marianne Stein at the University of Illinois, College of ACES. Accurately predicting when and where hunger crises occur is critical to effective humanitarian aid response. A new study from the University of Illinois explores how machine learning can help improve forecasting when used appropriately.

Current food insecurity predictions mostly rely on a system in which groups of experts gather together and assess food insecurity within countries. While the process includes some data to guide assessment, it remains mostly a qualitative evaluation based on local knowledge. “Our goal is not to overhaul this existing system, which has made incredible contributions across countries, generating predictions about food crises in places where there’s very little data and a lot of political complexity,” says Hope Michelson, associate professor in the Department of Agricultural and Consumer Economics at U of I and co-author on the paper.

The study asserts machine learning models can help provide critical information to assist the forecasting process, making it more objective, focused, and transparent. But the authors emphasize data must be used in a thoughtful way and interpreted correctly in conjunction with policymakers from the start.

“It’s really important to be working actively to improve the way we forecast food insecurity,” Michelson states. “And that requires researchers involving policymakers and policy priorities. We see a need for some harmonization and guiding principles in order to make those research efforts effective and implementable.”