The recent growth of Artificial Intelligence (AI) based systems considerably widespread their use in many application areas and our daily lives [28]. For instance, AI models are nowadays imbued into web search engines, self-autonomous vehicles, recommendation systems, games, and healthcare [24]. Accordingly, the demand for eXplainable AI (XAI) has risen [20] , [21] to help users cope with the growing complexity and opaqueness of the emerging generation of AI models [20] , [21]. By allowing users to perceive and make sense of the behaviors and outcomes of such models, XAI enables users of diverse levels of expertise to trust, manage, design, inspect, and develop AI models [13].