The proper Bayesian response would be to look for factors that are better predictors, since both "being male" and any race are very weak predictors (see my other comment upthread).
Sure, compute the odds ratio for committing a violent crime due to being male. It is large, which in plain English means "most violent criminals are male".
But that doesn't mean the probability of a randomly chosen male being a violent criminal is large. It's still small, because you have to multiply the odds ratio by a very small prior (since the prior probability of a randomly chosen person being a violent criminal is very small). So if all you know about a person is that they are male, you still have only a very weak prediction that they might be a violent criminal. In other words, in plain English, "most males are not violent criminals".
What you need to have a strong predictor is some factor X for which not just the odds ratio but the posterior probability of being a violent criminal, conditioned on X, is large, i.e., for which you can say "most X's are violent criminals". Being male is not such a factor.
I saw your comment - you don't understand how bayesianism works. Look up "odds ratios" for the most salient concept that you're missing here.
If P(x|!y) = 0.0000001 and P(x|y) = 0.01, this relationship is extremely powerful even though it's "weak" in the sense you're using.