This classification is very useful to discuss this issue.
The difference between 3 and 4, noble as it is, can be caused by feasability concerns that push people into 3, not just ignorance of the privacy impact. Human labelling of training data sets is a big thing in supervised learning. Methods that dispense with this would be valuable for purely economic reasons beyond privacy - the cost of human labelling of data samples. Yet we don't have them!
Techniques like federated learning or differential privacy can train models on opaque (encrypted or unavailable) data. This is nice, but they assume too much: that the data is already validated and analyzed. In real life modelling problems, one starts with an exploratory data analysis, the first step being looking at data samples. Opaque encrypted datasets also stop ML engineers from doing error analysis (look at your errors to better target model/dataset improvements) which is an even bigger issue, IMO, as error analysis is crucial when iterating on a model.
Even for an already productivized model, one has to do maintenance work like checking for concept drift, which I can't see how to do on an opaque dataset.