Explainable AI: current status and future directions

For explainability, we generally try to provide the explanation on the basis of the selection and rejection of the specific alternatives or outcomes. For given scenario, why only outcome A selected not B. A useful tool to provide such a discriminative explanation is using counterfactuals. We can use counterfactuals to provide reasonably valid arguments at the end of the conclusion by machine learning model which is supported by either deep learning or classical statistical modeling. With the nature of counterfactuals, a certain set of features are defined that can change the decision of the model. If those features are not available then the final conclusion of the model will be changed.