The rapid advances of Artificial Intelligence (AI) led to the development of systems that are yet to be incorporated in critical and high-impact decisions and processes. The sensitivity of these decisions has driven the need of developing methods and techniques to explain the nature of the AI system’s outcomes. This effort is driven on one hand by the aspiration and need to create trustworthy AI and on the other hand by the laws and regulations which require explanations of the logic underlying the outcomes. This panel will discuss the need for explainable artificial intelligence (XAI), deliberate its four principles with focus on explanation accuracy and knowledge limitation, and provide a framework for our industry to follow when developing AI systems using genuine and practical case studies to exemplify XAI concepts.