> So if you feed it historical crime data, it will pick out the patterns associated with crime. But those patterns are statistical correlations—nowhere near the same as causations. If an algorithm found, for example, that low income was correlated with high recidivism, it would leave you none the wiser about whether low income actually caused crime. But this is precisely what risk assessment tools do: they turn correlative insights into causal scoring mechanisms.<p>My main problem with this sort or argument is that it compares an algorithm to some ideal. As though a judge has some infallible insight from carefully studying empirical results for recidivism.<p>In regards to correlation vs causation, they're important when trying to prescribe solutions. For instance, suppose that higher rates of recidivism are caused by growing up in an abusive household. And having a violent parent also causes a lower income. Correlation vs causation is important when trying to remedy the problem. Giving the family money will not solve the underlying problem, just a side effect. Solving the side effect may still be worth it though. But if you're determining rates of recidivism, it would be better to look at the underlying cause (did you grow up in a violent household) rather than a side effect (poverty), but they would both lead to the same final recidivism score. And you can still reject both under the premise that the past that's out of your control should not be considered.<p>But even if you are set on only using the cause, statistical methods are more likely to find these results. And you can easily control what input is provided. You can tell a judge to ignore certain characteristics, but her actual reasoning process is likely opaque.