Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy
It addresses fairness and representation problems in poverty mapping for policymakers, but is incremental as it focuses on analyzing existing methods rather than introducing new ones.
This paper investigated disparities and biases in satellite-based poverty maps across urban and rural areas in ten countries, finding that these issues affect the validity of policies relying on such maps, such as aid allocation.
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.