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Sorry to ask, but I don't understand the points you are making. Could you please elaborate on them? I had trouble with the last 2 paragraphs. Thanks
AI explainability is a concept that's being thrown around frequently these days; it revolves around the idea that machine learning models should be "explainable", that is, their predictions should be traceable not just to the mathematical operations that define it, but also to some properties of the input which should be understandable by a human. While I won't deny that the concept is interesting, it's terribly diff…
An example from class. Suppose you are building a ML decider which can stop a production line if it sees defective products. If you are choosing between a decision tree and a neural net, one of the things to consider is that with a DT you can look at the tree the model comes up with a d say, okay if the mass of the widget is low, we reject.
With a NN, you can't see why things are rejected in the same way.
Some tasks benefit from having more explainable models, some it doesn't matter. But I don't think it's just a buzzword or trying to enforce political control.