Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of ``ground truth" data from surveys, and then predicting poverty levels in areas where imagery exists but surveys do not. Using survey and satellite data from ten countries, this paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines, and shows how these phenomena affect the validity of policies based on predicted maps. Our findings highlight the importance of careful error and bias analysis before using satellite-based poverty maps in real-world policy decisions.

翻译：基于卫星图像的贫困地图越来越多地被用于指导高风险政策决策，例如人道主义援助的分配和政府资源的发放。这类贫困地图通常通过利用相对少量的调查“地面真实”数据训练机器学习算法构建，随后在拥有卫星图像但缺乏调查数据的区域预测贫困水平。本研究利用来自十个国家的调查与卫星数据，考察了卫星贫困地图在城乡维度上的代表性差异、预测误差的系统性偏差以及公平性问题，并揭示了这些现象如何影响基于预测地图制定的政策的有效性。我们的研究结果强调，在将卫星贫困地图应用于现实政策决策之前，必须进行审慎的误差与偏差分析。