Poverty map inference has become a critical focus of research, utilizing both traditional and modern techniques, ranging from regression models to convolutional neural networks applied to tabular data, satellite imagery, and networks. While much attention has been given to validating models during the training phase, the final predictions have received less scrutiny. In this study, we analyze the International Wealth Index (IWI) predicted by Lee and Braithwaite (2022) and Esp\'in-Noboa et al. (2023), alongside the Relative Wealth Index (RWI) inferred by Chi et al. (2022), across six Sub-Saharan African countries. Our analysis reveals trends and discrepancies in wealth predictions between these models. In particular, significant and unexpected discrepancies between the predictions of Lee and Braithwaite and Esp\'in-Noboa et al., even after accounting for differences in training data. In contrast, the shape of the wealth distributions predicted by Esp\'in-Noboa et al. and Chi et al. are more closely aligned, suggesting similar levels of skewness. These findings raise concerns about the validity of certain models and emphasize the importance of rigorous audits for wealth prediction algorithms used in policy-making. Continuous validation and refinement are essential to ensure the reliability of these models, particularly when they inform poverty alleviation strategies.

翻译：贫困地图推断已成为研究的关键焦点，其利用从回归模型到应用于表格数据、卫星图像和网络的卷积神经网络等传统与现代技术。尽管在训练阶段验证模型受到了广泛关注，但最终预测结果却较少受到严格审查。本研究分析了Lee与Braithwaite（2022年）和Espín-Noboa等人（2023年）预测的国际财富指数（IWI），以及Chi等人（2022年）推断的相对财富指数（RWI），覆盖六个撒哈拉以南非洲国家。我们的分析揭示了这些模型在财富预测中的趋势与差异。值得注意的是，即使在考虑训练数据差异后，Lee与Braithwaite的预测与Espín-Noboa等人的预测之间仍存在显著且出乎意料的差异。相比之下，Espín-Noboa等人与Chi等人预测的财富分布形态更为接近，表明其偏斜程度相似。这些发现引发了对某些模型有效性的担忧，并强调了严格审计用于政策制定的财富预测算法的重要性。持续验证与优化对于确保这些模型的可靠性至关重要，尤其是在它们为减贫策略提供依据时。