Computers Can Be Understood
Counterexample:Convolutional Neural Networks (CNNs) (or any ML model, really)We still don't fully understand how or why they work so well. If you have software that queries a CNN and the CNN returns some prediction, there's absolutely no way of knowing or understanding the reasoning behind the decision. It's a black box of magical layers and weights that have been "trained" to make good predictions.If you have fraud detection software using ML and it flags something as fraud, that's the extent of the information you get. In this case the computer cannot be understood - it cannot explain to you how it arrived at the conclusion that something is fraud, nor can you explain how the computer arrived at the decision. It just has huge tables of weights and biases that magically "know" when something is fraud.