It's not news in ML (machine learning) that whenever you do have a reasonable understanding/model of the underlying task and an algorithm for solving it, you sh

It's not news in ML (machine learning) that whenever you do have a reasonable understanding/model of the underlying task and an algorithm for solving it, you should use that and not try to apply ML to learn a model from data. If an acceptable non-ML solution exists, then usually nobody reasonable would suggest using ML (unless they just want to pad their resume) because the known algorithm will usually do the same thing with much less computation and more reliability and interpretability. However, there are many, many useful real world tasks where we haven't been able to do that (e.g. almost all of image analysis, image generation, a significant part of of text analysis and generation tasks), so for these tasks the (valid) criticism doesn't really matter, as we don't have any reasonable alternatives - if let the network develop its own model, we get a somewhat working solution (with all the risks you mention), and if we don't, then we best we can make is unusably bad. If all the windows in your car are painted opaque black except the rear window, then driving the car by looking out of the rear window to see where you have been can be as good as it gets.