Approach #1 is to just go to maximize expected value, using all variables, including race.
Approach #2 is to use race to adjust the distributions as you suggest. Call this "affirmative action."
The inbetween approach #1.5, that we usually follow, is to make a value judgment about whether any individual item is OK to use.
Income? Of course it's reasonable to use income to determine whether to lend-- even if it's correlated with race. Race? No, that seems wrong. Location? Sure--- wait, you've drawn a box around all the black neighborhoods, nope! Buys certain products? Sure-- wait, you don't mean "buys products that blacks stereotypically like," do you?
#1.5 is already iffy, but becomes completely untenable once model complexity gets high and we use approaches that do not do well at explaining their decision: it basically becomes #1.
A major problem with #1 is it becomes self-perpetuating and reinforcing: if everyone is going to be biased against you, you're going to do worse, and hence the models are validated/more models are created with these assumptions. Everyone using #1 may improve their own expected value at the expense of a worse outcome for society as a whole.