SHAP is an open source to provide explanations of “how models proivde results”. It is very useful to develop AI models and inplement them effectively.

This is from SHAP websile. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions.

The algorithms and visualizations used in this package came primarily out of research in Su-In Lee's lab at the University of Washington, and Microsoft Research.