> When our ML solutions are built on historical data, it learns those biases as well.

It's actually worse than that, because the algorithm is giving you the correct result.

For example, suppose that black men are more likely to fail to appear in part because they have worse, less flexible jobs because of racist hiring practices.

That isn't fair, but it's still true that they're more likely to fail to appear.

Are we supposed to give bail to people we know aren't going to show up because the reason they won't show up isn't fair?

That seems like an obviously bad idea, where the good idea is to do something about racist hiring practices so that doesn't happen anymore. At which point the algorithm will see the new data and un-bias itself.