Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability. We tackle the issue of model explainability in the context of prediction models. We analyze a dataset of loans from a credit card company and apply three stages: execute and compare four different prediction methods, apply the best known explainability techniques in the current literature to the model training sets to identify feature importance (FI) (static case), and finally to cross-check whether the FI set holds up under what if prediction scenarios for continuous and categorical variables (dynamic case). We found inconsistency in FI identification between the static and dynamic cases. We summarize the state of the art in model explainability and suggest further research to advance the field.

翻译：可解释人工智能（XAI）在分析建模中存在对应概念，我们称之为模型可解释性。本文针对预测模型中的可解释性问题展开研究。我们基于信用卡公司的贷款数据集，分三个阶段进行分析：首先执行并比较四种不同的预测方法；随后将当前文献中最前沿的可解释性技术应用于模型训练集，以识别特征重要性（静态场景）；最后通过连续变量与分类变量的假设预测场景交叉验证特征重要性集合的稳健性（动态场景）。研究发现静态场景与动态场景下的特征重要性识别存在不一致性。本文综述了模型可解释性领域的研究现状，并对推动该领域发展提出进一步的研究方向。