“We don’t like deep learning, because it is a black box and we cannot explain it.” Really? How many people actually know how their car engine, refrigerator or laptop works? How many people actually know how spaghetti or fried chicken are made? These are common everyday black boxes. And we feel totally comfortable using them every day even though we have no clue how they work. So what is the difference? Why it is okay to drive a car but not okay to use deep learning model? Because EVERYONE drives a car and not EVERYONE uses deep learning. That’s the only difference.

DL can be made less black box when needed. (If we're classifying images of felines, I'm not sure how transparent they need to be except to the developers.) Charu Aggarwal of IBM has suggested that gradients can be used to interpret the size and direction of predictions resulting from a change in an input. Sensitivity analysis, as used by statisticians, can be used in similar ways, as can statistical analysis of the DL's predictions.


Negative. All those other examples you listed have readily available experts that understand how it all works. In Deep Learning, even the brightest people who created a neural network have a difficult time explaining why their creation is working/not working. Neural networks are by definition convoluted.

Also, deep learning is often impractical and has very few applications.

https://link.medium.com/lsmcXVn8WX
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