Uncharted Technology
Prioritizing Food Security Interventions with Causemos

Global problems like food insecurity have many causes—weather, infestation, conflict—each endlessly studied. Because mapping the downstream effects is an immense effort, decision makers who remedy any one cause inevitably introduce unintended consequences.

In this application of Causemos, we used AI to accelerate the ability to uncover, synthesize, and apply knowledge from vast and disparate data collections. Powerful search capabilities allowed analysts to merge their qualitative perception of food insecurity with grounded quantitative data. These added up to granular visual indices that weighed all possible drivers and ranked the sensitivity to different outcomes. This approach enabled analysts to perceive and model the global impact of food insecurity and prioritize interventions in just hours instead of days or weeks.