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.

翻译：可解释人工智能为不可解释的机器学习模型提供解释。尽管存在许多技术方法，但这些技术在现实数据集上的验证仍显不足。本研究展示了一个可解释人工智能的应用案例：一个基于塞内加尔手机数据训练用于估算电气化率的机器学习模型。数据源自2014/15年Orange公司举办的"数据促进发展"挑战赛。我们应用两种与模型无关的局部解释技术，发现该模型虽然可被验证，但存在对人口密度的偏差。我们通过指出研究中遇到的两大挑战来总结论文：一是数据处理和模型设计可能受现有可解释人工智能方法的限制，二是领域知识对解释结果解读的重要性。