The problem is that the output of those algorithms is used to drive decision making that has the effect of maintaining the status quo, by removing opportunities to change it.
I think your comment shows that you understand this. You accept that a decision may be correct, when measured in totally cold and statistical terms. But such decisions would not "change the status quo" and that would be a problem.
But that position is a deeply political one. Why should decisions at banks, tech firms, or wherever be deliberately biased to change the status quo? It's social engineering, a field with a long and terrible track record of catastrophic failure. Failure both to actually change reality, and failure in terms of the resulting human cost.
Injecting bias into otherwise unbiased decisions by manipulating ML models, or by manipulating people (threatening them if they don't toe the line), is never a good thing.
The whole point of using the algorithm was to make sure personal biases aren't impacting the decision. If we're going to alter the algorithm because we don't like the result, then why are we bothering to use an algorithm in the first place? Just use a human to make the decision. At least in that scenario potential biases have an identifiable source, as opposed to an opaque program that may have been made by engineers that deliberately tuned it to avoid any disparities because they think any disparity in outcome is fundamentally problematic