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Machine learning for food security: Principles for transparency and usability

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  • Yujun Zhou
  • Erin Lentz
  • Hope Michelson
  • Chungmann Kim
  • Kathy Baylis

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.

Suggested Citation

  • Yujun Zhou & Erin Lentz & Hope Michelson & Chungmann Kim & Kathy Baylis, 2022. "Machine learning for food security: Principles for transparency and usability," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 893-910, June.
    • Handle: RePEc:wly:apecpp:v:44:y:2022:i:2:p:893-910
    DOI: 10.1002/aepp.13214
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    References listed on IDEAS

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    Cited by:

    1. Yi, Jing & Canning, Patrick N. & Ge, Houtian & Rehkamp, Sarah & Gomez, Miguel I., 2023. "A national database of highly perishable fresh produce production with temporal and spatial resolution," 2023 Annual Meeting, July 23-25, Washington D.C. 335590, Agricultural and Applied Economics Association.

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