Can artificial neural networks (ANN) provide detailed explanations of their capabilities?

Artificial neural networks (ANN) are often characterized by their complexity and non-linear structures, which contribute to their powerful predictive capabilities. However, this complexity comes at a cost – interpretability. ANNs are often described as "black boxes," meaning they can deliver predictions or classifications without providing clear insights into how they arrived at those conclusions. This lack of transparency makes it challenging to understand the decision-making process behind the model's outputs.

In many applications, especially in critical areas like healthcare or finance, stakeholders require explanations behind predictions to trust and validate the model's decisions. The intricate architecture of ANNs, which involves numerous layers and interconnected nodes, obscures the internal workings, making it difficult for practitioners to derive understandable and meaningful explanations of the model's behavior. This is why answers indicating that ANNs can always or sometimes provide detailed explanations or that they can do so for simple problems do not accurately represent the overall capabilities of these models.