Explaining risks: axiomatic risk attributions for financial models
针对金融领域高风险场景，提出将Shapley值框架扩展用于风险归因，公平分配各特征对模型风险的贡献，帮助解释黑箱模型的风险来源。
In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley value framework.