Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct objectives require distinct data and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning system development. Overall, we urge careful consideration of the purpose and use cases of machine learning informed models.

中文翻译：

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预测贫困和营养不良以进行定位、绘图、监测和预警

越来越丰富的数据和强大的预测算法提高了数据科学对人道主义和发展规划的承诺。我们提倡拥抱和投资机器学习方法，以解决贫困和营养不良的目标、制图、监测和预警，同时也警告说，不同的目标需要不同的数据和方法。特别是，我们强调了贫困和营养不良目标和映射之间的差异、结构性剥夺和随机剥夺之间的差异，以及早期预警系统开发的建模和数据挑战。总体而言，我们敦促仔细考虑机器学习知情模型的目的和用例。