I don’t quite understand how a more accurate DL model makes it more interpretable, but perhaps we mean different things when we use that term? The paper I linked above titled " Why do deep convolutional networks generalize so poorly to small image transformations?" ([1805.12177] Why do deep convolutional networks generalize so poorly to small image transformations?) was published in May, 2018 and evaluates pretty recent architectures and still shows very disparate model outputs for perceptually identical images. To me, explaining “why” is very difficult beyond stating that a combination of convolutions/multiplications involved in the architecture just results in disparate numeric answers. So that feels like a lack of interpretability in the model.
I’m not trying to knock DL or discourage folks from it (I wouldn’t be here otherwise!), but I think a certain amount of thought needs to go into whether it is the best solution and it is also good to retain some skepticism and be aware of the kinds of things that can go wrong with them. To the original point in my post, most people (including myself) feel that CNNs have excellent insensitivity to translation of objects in an image, and yet the paper above clearly shows that that intuition is a bit flawed and can result in very surprising outcomes. Being aware of these types of potential gotchas is always a good thing imho.
Fair enough, but I wasn’t trying to say that logistic regression was suited for self-driving cars. I was trying to (perhaps inarticulately) point out that deep-learning is not the be all and end all of all ML and in certain domains/problems, simpler, more interpretable models combined with domain knowledge + feature engineering can still provide better or at least similar performance while still being far more easy to interpret… the Electronic Health Records paper from Google seems to be a decent example of that - Scalable and accurate deep learning with electronic health records | npj Digital Medicine