ExplAInable AI

“The inner workings of many deep learning systems are complicated if not impossible for the human mind to comprehend. ExplAInable Artificial Intelligence (XAI) AIms to provide AI experts with transparency into these systems.
In this course you ll describe what ExplAInable AI is how to use it and the data structures behind XAI s preferred algorithms. Next you ll explore the interpretability problem and today s state-of-the-art solutions to it. You ll identify XAI regulations define the "right to explanation" and illustrate real-world examples where this has been applicable.
You ll move on to recognize both the Counterfactual and Axiomatic methods distinguishing their pros and cons. You ll investigate the intelligible models method along with the concepts of monotonicity and rationalization. Finally you ll learn how to use a Generative Adversarial Network.”