Shapley-Lorenz eXplainable Artificial Intelligence

作者：

Highlights：

• A new global eXplainable Artificial Intelligence method is proposed.

• Our method is based on the use of Shapley values and Lorenz Zonoid decomposition.

• The derived variable importance criterion fulfills explainability requirement.

• The application to bitcoin data shows the above mentioned advantages.

摘要

•A new global eXplainable Artificial Intelligence method is proposed.•Our method is based on the use of Shapley values and Lorenz Zonoid decomposition.•The derived variable importance criterion fulfills explainability requirement.•The application to bitcoin data shows the above mentioned advantages.

论文关键词：Shapley values,Lorenz Zonoids,Predictive accuracy

论文评审过程：Received 10 May 2020, Revised 29 July 2020, Accepted 6 October 2020, Available online 16 October 2020, Version of Record 10 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114104