You must hate neural networks, then...

Yep. First they came for the routine task, non-cognitive workers and I said nothing....

I understand neural networks are producing excellent results these days, for example with "deep learning". I don't know a lot about them. I believe interest in them took off years ago when the mathematics that modeled them proved they would produce certain results for certain problems under certain conditions.


This looks like a nice article about them:
https://medium.datadriveninvestor.co...s-89fb50622429
From the link:
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Most of the times a neural network will give you good results if you are using it for the right problem, but if it doesn’t perform well, you will have a lot of trouble finding why it didn’t go as expected, especially a Deep Neural network(which will be the case most of the times). For example, if you were trying to predict the type of cancer and you expected the output to be Malignant, but instead you got Benign, it would be quite hard to figure out why the neural network gave such an input, instead a traditional ML algorithm such as a Decision Tree, will be much more easy to interpret.

Example: Banks generally will not use Neural Networks to predict whether a person is creditworthy because they need to explain to their customers why they denied them a loan.

Long story short, when you need to provide an explanation to why something happened, Neural networks might not be your best bet. You simply cannot take a decision just because your computer said so.
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Here's the Wiki:
https://en.wikipedia.org/wiki/Artificial_neural_network


PairTheBoard

See the bold italicized in the last paragraph.

To go back to your statement:

Credibility is in the results, not the reasons.