Explaining Outputs Of Algorithms With The Help Of Explainable Artificial Intelligence

Explainable AI is a type of AI that enhances the understanding of AI algorithms’ decision-making processes by introducing transparency and accountability. It provides a means to unravel the complexities of AI decision logic, fostering trust and facilitating meaningful human oversight. The post-modeling explainable AI method is also called a post-hoc method and is developed around four important components, namely, the target, the drivers, the explainable family, and the estimator.