  Poverty map inference is a critical area of research, with growing interest
in both traditional and modern techniques, ranging from regression models to
convolutional neural networks applied to tabular data, images, and networks.
Despite extensive focus on the validation of training phases, the scrutiny of
final predictions remains limited. Here, we compare the Relative Wealth Index
(RWI) inferred by Chi et al. (2021) with the International Wealth Index (IWI)
inferred by Lee and Braithwaite (2022) and Esp\'in-Noboa et al. (2023) across
six Sub-Saharan African countries. Our analysis focuses on identifying trends
and discrepancies in wealth predictions over time. Our results show that the
predictions by Chi et al. and Esp\'in-Noboa et al. align with general GDP
trends, with differences expected due to the distinct time-frames of the
training sets. However, predictions by Lee and Braithwaite diverge
significantly, indicating potential issues with the validity of the model.
These discrepancies highlight the need for policymakers and stakeholders in
Africa to rigorously audit models that predict wealth, especially those used
for decision-making on the ground. These and other techniques require
continuous verification and refinement to enhance their reliability and ensure
that poverty alleviation strategies are well-founded.
