Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model
Jiarui Li, Tetsuo Sawaragi, Yukio Horiguchi
Abstract
With the development of artificial intelligence technologies, the high accuracy of machine learning methods has become a non-unique standard. People are beginning to be more concerned about the understandability between humans and machines. The interference procedure of the machines is hoped to accord with human thinking as much as possible, which has spawned the recent and ongoing demands for developing explainable models. The present study proposes a new explainable and persuasive model for machine learning problems by introducing Structural Equation Modelling into the picture. Six parts make up the model, from data collection to model evaluation. The model can be used for data analysis, machine learning, and causal analysis. The proposed model is also transparent and can be interpreted from design to application. A practical experiment shows its effectiveness in a healthcare problem.