论文标题
实践中的解释性：塞内加尔手机数据的电气率估算
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
论文作者
论文摘要
可解释的人工智能（XAI）为无法解释的机器学习（ML）模型提供了解释。尽管存在许多技术方法，但缺乏对现实世界数据集上这些技术的验证。在这项工作中，我们提出了一个XAI：ML模型的用例，该模型经过培训，可以根据塞内加尔的手机数据估算电气化率。数据源自Orange在2014/15年度的开发挑战数据。我们采用两种模型不合时宜的局部解释技术，发现虽然可以验证该模型，但它与人口密度相比有偏差。我们指出了在工作中遇到的两个主要挑战来结束论文：数据处理和模型设计可能受到当前可用的XAI方法的限制，以及域知识对解释解释的重要性。
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.
