It sounds to me like, in the claim “deep learning is uninterpretable”, the key word in “deep learning” that makes this claim true is “learning”, and you’re substituting the similar-sounding but less true claim “deep neural networks are uninterpretable” as something to argue against. You’re right that deep neural networks can be interpretable if you hand-pick the semantic meanings of each neuron in advance and carefully design the weights of the network such that these intended semantic meanings are correct, but that’s not what deep learning is. The other things you’re comparing it to that are often called more interpretable than deep learning are in fact more interpretable than deep learning, not (as you rightly point out) because the underlying structures they work with is inherently more interpretable, but because they aren’t machine learning of any kind.
It sounds to me like, in the claim “deep learning is uninterpretable”, the key word in “deep learning” that makes this claim true is “learning”, and you’re substituting the similar-sounding but less true claim “deep neural networks are uninterpretable” as something to argue against. You’re right that deep neural networks can be interpretable if you hand-pick the semantic meanings of each neuron in advance and carefully design the weights of the network such that these intended semantic meanings are correct, but that’s not what deep learning is. The other things you’re comparing it to that are often called more interpretable than deep learning are in fact more interpretable than deep learning, not (as you rightly point out) because the underlying structures they work with is inherently more interpretable, but because they aren’t machine learning of any kind.