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Explainable artificial intelligence (XAI) provides explanations for notinterpretable machine learning (ML) models. While many technical approachesexist, there is a lack of validation of these techniques on real-worlddatasets. In this work, we present a use-case of XAI: an ML model which istrained to estimate electrification rates based on mobile phone data inSenegal. The data originate from the Data for Development challenge by Orangein 2014/15. We apply two model-agnostic, local explanation techniques and findthat while the model can be verified, it is biased with respect to thepopulation density. We conclude our paper by pointing to the two mainchallenges we encountered during our work: data processing and model designthat might be restricted by currently available XAI methods, and the importanceof domain knowledge to interpret explanations.

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