Description: The Explanatory Framework in the context of Explainable Artificial Intelligence (XAI) refers to a structured approach that seeks to provide clarity and understanding about the decisions and predictions made by artificial intelligence models. This framework focuses on breaking down the internal processes of algorithms, allowing users to understand not only the final outcome but also the factors and logic that led to that conclusion. The importance of this approach lies in the need for trust and transparency in automated systems, especially in critical applications such as healthcare, law, and finance. By offering comprehensible explanations, the Explanatory Framework helps mitigate the phenomenon of the ‘black box’ that often characterizes complex AI models, where decisions are difficult to interpret. This framework not only focuses on the accuracy of predictions but also on interpretability, ensuring that users can make informed decisions based on the information provided by the model. In summary, the Explanatory Framework is essential for fostering acceptance and responsible use of artificial intelligence across various sectors, promoting a more open dialogue between humans and machines.
