Algorithms don't think for themselves, and AFAIK, the COMPAS algorithm has not been touted as one of those self-healing types. We wouldn't know either way because the algorithm is kept secret even though it is broadly applied in the public justice system.
But let me get this straight, you'd be OK with an algorithm just using the heuristic of "well, he's black, so give him a -0.5, because that's what blacks tend to do" to avoid the complexity of identifying possible confounding variables that are correlated with being black? That's actually illegal in many contexts (judicial system, housing, employment).
According to the PP article, race is not an input to the algorithm so it can't give -0.5 to black people solely for being black.
It's the other way around. If you only use the other variables but most of the variables that correlate with recidivism also correlate with being black, the algorithm will still predict higher recidivism rates for black people even if it doesn't know anything about skin color.
The more accurate the algorithm is the more it reflects the correlations that exist in the actual outcomes.
Which is a different matter entirely. I don't think there is any good argument that they shouldn't have to publish the algorithm.
> I'm going to assume that race is not actually used as a weight, I would still like to see the methodology used in determining the validity of the other weights, some of which may act as a proxy for race while not having a significant causal effect on recidivism.
The problem is they're all proxies. Homelessness likely correlates with recidivism, but it isn't the homelessness that causes it, it's the poverty and lack of a support network it implies. Someone with affluent close friends who chooses to live outside because they enjoy communing with nature is in a different situation entirely. But you can measure whether someone has a street address more easily than you can measure their support network, so it gets used as a proxy.
I'm not sure causal effect is the thing you want to hang your hat on anyway, otherwise the existence of racism would imply that race has a causal effect on recidivism and should be used as a factor, which I take it is not the outcome you were after.
> Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts
Which is exactly what you would expect to happen if the algorithm predicts that black defendants are more likely to recidivate. Being in the group predicted to recidivate more will make you more likely to be predicted to recidivate. It would be quite unexpected for that not to still hold for the people who didn't actually recidivate.
And if being black does correlate with recidivism, what is the algorithm supposed to do?