Here's something I really dislike about all the coverage I've seen about these "risk assessment algorithms": There is absolutely no discussion of the magnitude

Here's something I really dislike about all the coverage I've seen about these "risk assessment algorithms": There is absolutely no discussion of the magnitude of the distinctions between classifications. Is "low risk" supposed to be (say) 0.01% likelihood of committing another crime and "high risk" (say) 90%? Or is "low risk" (say) 1% vs. "high risk" of (say) 3%? Having worked on human some predictive modeling of "bad" human events (loan defaults) my gut says it's more like the latter than the former, because prediction of low-frequency human events is really hard, and, well, they're by definition infrequent. If that suspicion is right, then the signal-noise ratio is probably too poor to even consider using them in sentencing, and that's without considering the issues of bias in the training data, etc. But there is never enough detail provided (on either side of the debate) for me to make an informed assessment. It's just a lot of optimism on one side and pessimism on the other. I'd really love to see some concrete, testable claims without having to dive down a rabbit hole to find them.