Machine learning for food security: Principles for transparency and usability. Issue 2 (30th November 2021) - Zhou, Yujun; Lentz, Erin; Michelson, Hope; Kim, Chungmann; Baylis, Kathy
Abstract: Machine learning (ML) holds potential to predict hunger crises before they occur. Yet, ML models embed crucial choices that affect their utility. We develop a prototype model to predict food insecurity across three countries in sub‐Saharan Africa. Readily available data on prices, assets, and weather all influence our model predictions. Our model obtains 55%–84% accuracy, substantially outperforming both a logit and ML models using only time and location. We highlight key principles for transparency and demonstrate how modeling choices between recall and accuracy can be tailored to policy‐maker needs. Our work provides a path for future modeling efforts in this area.