README.md

## Machine learning for food security: Principles for transparency and usability

**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 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.
 
**Keywords**: food insecurity, machine learning, early warning, Sub-Saharan Africa, famine