Description: The interpretability of neural networks refers to the degree to which a human can understand the decisions made by a neural network. This concept is crucial in the field of explainable artificial intelligence (XAI), where the aim is to unravel the internal processes of complex models that often operate as ‘black boxes’. Interpretability allows users to comprehend how and why certain predictions or decisions are generated, which is fundamental in critical applications such as healthcare, law, and finance. The main characteristics of interpretability include transparency, the ability to explain decisions in an understandable manner, and the possibility of verifying the logic behind conclusions. The relevance of this concept lies in the need for trust and accountability in the use of AI systems, especially in contexts where decisions can significantly impact people’s lives. Without adequate interpretability, users may distrust automated systems, limiting their adoption and effectiveness. Therefore, research in interpretability seeks to develop methods and tools that make neural network models accessible and understandable, thus promoting a more ethical and responsible use of artificial intelligence.
