Computer scientists generally struggle with intensional contexts, ie., the sense in which sqrt(4) isnt (1 + 1); or the sense in which I believe(that sqrt(1101) ~10) but I dont believe(that 10^2 ~1101)

So they replace everything with extensional contexts, ie., in/out sets -- where things are replaceable by their values. And then use the machinery of very simple mathematics to analyse the situation.

In ML, consider comparing two classifiers extensionally, so that two classifiers "are the same" if they partition some input data exactly the same vs., declaring them different if they do so by means of some differing internal representation.

Since intensional contexts are the archetypal feature of mental representations, they're rarely analysed well by computer scientists.

I think its easy for us to think we can understand what a program is going to do when we're dealing with high level abstractions. I think if we really came to grips with everything the program is doing all the way down to the hardware level, we might have less confidence that we can predict what its going to do with 100% accuracy every time, even in the case of supposedly simple programs.