We know why these models make decisions on data. They're optimizing for lower error rates. The black box unveiled for a convolutional neural network is this: o

We know why these models make decisions on data. They're optimizing for lower error rates. The black box unveiled for a convolutional neural network is this: over the course of several thousand rounds of performing the dot product of the RGB values of a given training datum against a weight vector, this network has determined that a weight vector containing these values optimally reduce the error produced by "squashing" the output matrices of said dot products in a softmax function when the "squashed" value is compared against the pre-determined true value. If you would also like to be able to correctly predict whether a given input is part of the class for which this model was optimized for, we suggest your weight vectors also contain these values as this will reduce the number of false positives and false negatives you will produce from your prediction.