One of my big beefs with ML/AL is that these tools can be used to wrap bad ideas in what I will call “Machine legitimacy”. Which is another way of saying that there are many cases where these models are built up around a bunch of unrealistic assumptions, or trained on data that is not actually generalizable to the applied situation but will still spit out a value. That value becomes the truth because it came from some automated process. People cant critically interrogate it because the bad assumptions are hidden behind automation.

Meh, its not a perfect correlation (and the time series for the poverty map and the diabetes map are different), but most chronic diseases tend correlate with poverty pretty well. You should look at a map of obesity. It follows the same form.

That is really just a map of poverty.