Explainable AI

Shapley Additive Explanations (SHAP) is a game-theoretic approach to interpret the output of any machine learning model. It aims to explain the contribution of each feature to the final prediction of the model, thereby providing a clear understanding of how the model makes its decisions.

Explainable AI refers to models that can easily be interpreted by humans. Bayesian inferencing is a natural choice to obtain explanations of trained deep learning models.