Say in your example of criminality, if black people were more predicted by the classifier, it isn't racist if it accurately reflects the base distribution. There's a line to be crossed here, and to me it's if that application starts to significantly (where the boundary lies here is up for debate), and directly affect the non-criminal portion. I'd say that if the misclassification error is similar across racial lines, then there is no issue.
Additionally, I don't quite agree with the "making racial biases worse" argument either. The way I see it, we already use racial heuristics in law enforcement. With automated, replicable tasks, we can at least quantify the degree of bias and correct for such.
Most people would probably opt for utility mixed with representational fairness of some degree even when it means law applies to some degree differently to groups and special cases.
Justice is not fairness. It has an institution called motive. (Which is often overly simplified or ignored.)
It incorporates elements of fairness. And it is hard to train. Not everyone can be Solomon, for example.
https://www.newscientist.com/article/2114900-concerns-as-fac...
Trying to identify potential for criminality based on looks may well work, identifying and using the underlying racial biases. And if those systems are used, where people identified by these systems are more likely to be identified as criminals and investigated, we end up with a feedback loop and, over time, the racial biases in a society that uses the systems will get worse. More black criminals will be caught, as they are more likely to be suspected as criminal, making the racial biases worse for blacks. While the opposite happening for white faces.
Similar social evolution would happen if you pre-screened job candidates, over time magnifying existing gender and racial biases. I've seen in some cultures it is required to add photographs to job applications, but I think it a good thing that this practice is discouraged in western countries.