  Explainable artificial intelligence (XAI) provides explanations for not
interpretable machine learning (ML) models. While many technical approaches
exist, there is a lack of validation of these techniques on real-world
datasets. In this work, we present a use-case of XAI: an ML model which is
trained to estimate electrification rates based on mobile phone data in
Senegal. The data originate from the Data for Development challenge by Orange
in 2014/15. We apply two model-agnostic, local explanation techniques and find
that while the model can be verified, it is biased with respect to the
population density. We conclude our paper by pointing to the two main
challenges we encountered during our work: data processing and model design
that might be restricted by currently available XAI methods, and the importance
of domain knowledge to interpret explanations.
