Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy.

Emily Aiken

Abstract:

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.