That's kind of my point, that they have different tradeoffs, although I do not hide my preference for explainability (or model soundness). :-) The example you give reminds me of Chomsky and Norvig debate on the topic.

No, accuracy is but one possible goal. "Machine learning practitioners take pride in models that optimize the value of that model."

And since we cannot define exactly what this value is (accuracy, RMSE, bias or a specific confusion matrix), we have to make a more abstract definition.

We are now left with a question. Is this abstract definition quantifiable? If so, we can still exclude the statistician w.r.t. understandably. However, if we allow qualitative value as well, the traditional statistician is back at the table.

Now, a further refinement needs to be made. Since fairness, accountability and transparency in Data Science are in the limelight, we can make a point that our ability to understand is a key metric for Machine Learning models as well. It is interesting to see the tendency to associate words or phrases to key neurons in embeddings. For example, in embeddings of faces, we can associate gender, ethnicitity, fatness and gaze with some of the embedded neurons.

Statisticians take pride in models that are understandable, while machine learning practitioners take pride in models that are profitable.

On the +J+

Not sure why you're downvoted. Also noticed that everyone uses statistics because you're expected to, but hardly anyone knows how to do it right.

Most scientists who use statistics are not statisticians.

"statistical significance" - it has too many problems. A colleague complained once, that his test kept alternating between statistical significant and not significant back and forth. I know it's not how it is supposed to work, but most people using it don't know (or care) how to use it. My opinion: just don't use this concept at all.

I don't know. My former ML profs do a lot of science too...