In the context of Neural Networks, the "Hidden Layers" are where the actual transformation and feature extraction occur. These layers sit between the input and output, processing data through weighted connections to identify complex patterns. For an IS auditor, evaluating the hidden layers is critical because they represent the "logic" of the model that emulates human-like cognition.
While input and output layers are transparent, the hidden layers often lack interpretability, leading to risks of hidden bias or non-deterministic behavior. Understanding the depth and activation functions of these layers helps auditors assess the model's complexity and its susceptibility to errors.