  Recent advances in artificial intelligence and machine learning have created
a step change in how to measure human development indicators, in particular
asset based poverty. The combination of satellite imagery and machine learning
has the capability to estimate poverty at a level similar to what is achieved
with workhorse methods such as face-to-face interviews and household surveys.
An increasingly important issue beyond static estimations is whether this
technology can contribute to scientific discovery and consequently new
knowledge in the poverty and welfare domain. A foundation for achieving
scientific insights is domain knowledge, which in turn translates into
explainability and scientific consistency. We review the literature focusing on
three core elements relevant in this context: transparency, interpretability,
and explainability and investigate how they relates to the poverty, machine
learning and satellite imagery nexus. Our review of the field shows that the
status of the three core elements of explainable machine learning
(transparency, interpretability and domain knowledge) is varied and does not
completely fulfill the requirements set up for scientific insights and
discoveries. We argue that explainability is essential to support wider
dissemination and acceptance of this research, and explainability means more
than just interpretability.

Computers and Society;Artificial Intelligence;General Economics;Economics