Attentiveness on criticisms and definition about Explainable Artificial Intelligence

The emergence of deep learning at the beginning of the last decade has driven the advancement of complex models, culminating in the development of large language models and generative AI. These models represent the summit of size and complexity. Explainability should be an option that plays a key role in enabling understandable the AI-assisted decision-making and ensuring accountability. This contribution delves into the complexities of explainable artificial intelligence (XAI) through various perspectives, considering the extensive and growing body of literature. Our discussion begins by addressing the challenges posed by complex data, models, and high-risk scenarios. Given the rapid growth of the field, it is essential to tackle the criticisms and challenges that emerge as it matures, requiring thorough exploration. This contribution explores them, along with three aspects that may shed light on them. First, it is focused on the lack of definitional cohesion, examining how and why is defined XAI from the perspectives of audience and understanding. Second, it explores XAI explanations, bridging the gap between complex AI models and human understanding. Third, it is crucial to consider how to analyze and evaluate the maturity level of explainability, from a triple dimension, practicality, governance and auditability.