> The problem is the externalities of the policy. Not some mode overfitting. Sure I don't disagree with this. I guess the point is not so much that ML models a

> The problem is the externalities of the policy. Not some mode overfitting. Sure I don't disagree with this. I guess the point is not so much that ML models are bad here in the typical sense (although greedy consultants may, and IMO likely are, happy to pawn off shitty models to jurisdictions which don't know any better), but more that the underlying system isn't one where predictive modeling is truly going to be "effective" (although, as another commenter pointed out, there are cases where predictive modeling works fairly well such as pretrial detention risk assessment). The problem as I see it is that model "efficacy" means one thing to the cops and voters ("effective" in the sense of reducing or preventing crime) and another thing to a data science ("effective" in the sense of able to achieve a high F1 score or w/e). These definitions may be correlated, but are not guaranteed to be, and the strength of the correlation is highly dependent on how the model is ultimately used.