Explainability in Practice : Estimating Electrification Rates from Mobile Phone Data in Senegal

Explainable artificial intelligence (XAI) provides explana- tions 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 Develop- ment challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be veri- fied, 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 cur- rently available XAI methods, and the importance of domain knowledge to interpret explanations.