Machine learning for food security: Principles for transparency and usability

Yujun Zhou, Erin Lentz, Hope Michelson, Chungmann Kim and Kathy Baylis

Applied Economic Perspectives and Policy, 2022, vol. 44, issue 2, 893-910

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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1002/aepp.13214

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apecpp:v:44:y:2022:i:2:p:893-910

Access Statistics for this article

More articles in Applied Economic Perspectives and Policy from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().