Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
This work addresses the validation gap in XAI for practitioners, but it is incremental as it applies existing methods to a specific domain without major technical advancements.
The authors tackled the problem of validating explainable AI techniques on real-world data by applying model-agnostic methods to estimate electrification rates from mobile phone data in Senegal, finding that the model was biased with respect to population density.
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.