> When you train a neural network, it is also not really a black box. You can exactly see what it does, and inspect all weights and activations. You have a very

> When you train a neural network, it is also not really a black box. You can exactly see what it does, and inspect all weights and activations. You have a very clear logical reasoning why you end up with some decision. Only that this is maybe a huge decision rule when written out. In some networks eg. CNN you may be able to visualize which of the inputs have the greatest impact on the output(s) but that is a far cry from understanding how the network got there in the first place. When a network gets something consistently wrong the answer is usually "more/better training data" not a deep understanding of why the network is wrong. This is in contrast to a simple decision tree, whose decisions can be understood by even a layperson with no statistics background. It is simple to go back and understand why the wrong decision was made.