What is Berk's model? How well does it do across different risk bands? What variables are fed into it in the states where it is used? How does prediction succes

What is Berk's model? How well does it do across different risk bands? What variables are fed into it in the states where it is used? How does prediction success vary across types of crimes, versus demographics within crime? This article treats ML like a magic wand, which it isn't. There's not enough information to make a judgement on whether the tools are performing well or not, or whether that performance, or lack of it, is based on discrimination. Where we do have information it is worrying: "Race isn’t an input in any of his systems, and he says his own research has shown his algorithms produce similar risk scores regardless of race." What?!? The appropriate approach would be to include race as a variable, fit the model, and then marginalise out race when providing risk predictions. Confounding is mentioned but the explanation of how it is dealt with, without doing the above isn't given - just a (most likely false) reassurance.