It’s questionable how “immature” ML really is. Most methods that get used were initially designed 50+ years ago, with various improvements over time. E.g., neural networks were invented in the 1950s, backprop was introduced in the 80s, architectures like LSTM and CNN in the 90s, etc.

The only thing that’s really new is the amount of computational power at our hands. That has allowed us to shift from relatively simpler methods to more powerful but opaque methods like NNs. They just don’t lend themselves to easy analysis because it’s a lot harder to explain why inputs to these ML systems map to their respective outputs. Hence, attempts at drawing the connection between inputs and outputs become more speculative.

Sure people had these ideas, but there was no consensus that they were the right ones. As such they weren't studied as much as they are now.

The people who make a paper have to know where the border is for their particular paper. That is, which things in the paper are claims with evidence to back it up - and which things are speculations about what might be an explanation. That some things are speculative is not such a big deal, as long as it is clearly marked as such. Then someone else can investigate it properly in another paper. Or people can use it in another work, by treating it as an assumption that they can verify whether holds, and then make use of.