Then shouldn't the algorithms incorporate the unbiased data rather than incorporate something we know is biased to correct for other biases?

A question. How do we know the algorithm is biased if it give a racial disparate result? Don't tell me the evidence is that the result is racial disparate.

There usually isn't unbiased data (think: law enforcement databases), though you could maybe remove the variables more likely to be discriminated against and train on that

There are statistical analysis that can answer your second question, and it's not only based on disparate results (though that's probably part of it). And the solution is not "changing the signal" of the bias

If there are statistical analysis that can answer if the data is biased why isn't the focus of attention all biases rather than just biases that may result in racial discrimination?

Shouldn't all the arguments be over the data and any biases present (and how to obtain unbiased data) and not the result?