Description: A black box model is an algorithm whose internal mechanisms are not transparent or interpretable. This means that while one can observe the input and output of the model, it is not possible to understand how a particular decision or result is reached. This concept is especially relevant in the field of artificial intelligence, where many algorithms, such as deep learning models, operate in complex and nonlinear ways. The lack of interpretability can generate distrust in automated systems, especially in critical applications such as healthcare, criminal justice, or finance. The difficulty in unraveling the internal workings of these models poses ethical and practical challenges, as users and regulators require clear explanations of how decisions are made. Therefore, the development of explainable artificial intelligence (XAI) techniques seeks to address these concerns by providing tools and methods that allow users to better understand the behavior of black box models. In summary, a black box model represents an area of tension between the complexity of modern algorithms and the need for transparency and trust in their applications.
