What the eXplainable AI is?

Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. SHAP (SHapley Additive exPlanations) is a framework that explains the output of any model using Shapley values, a game-theoretic approach often used for optimal credit allocation. While this can be used on any black-box model, SHAP can compute more efficiently on specific model classes (like tree ensembles).