The problem of stereotyping is that people take a complex problem and attempts to reduce it by measuring people based on one or a few bits of information.

Crime is a bit like rain. The more one attempt to zoom closer to the atom the less we understand it and the poorer the prediction becomes. It get even worse when attempting to zoom in order to understand rare events.

If we look at the specific crime of rape in Sweden we see that P(person is Muslim | person is a rapist) > P(Person is a Christian | person is a rapist), by around 300%. The political reaction to people noticing the data is a bit volatile to say the least.

It is possible to still use the data, but its best to zoom out rather than in. A common one for crime is the acknowledgement that high risk groups tend share a trait of low social economic status. Thus a popular general prediction method is to measure social economic status when determining risk. What we then get is a more general P(person is socially isolated AND low income AND low education | person commit a crime) that we can compare to other prediction models. People then take each of those classifications and zooms out even further by addressing them independently and outside of crime prevention as improving them has value in itself.