The issue is that the pattern matching reinforces the problem based on race when race is not the cause. There are lots of historical reasons someone could be be a bad credit risk for example, but it isn’t because they’re black or some other race.
You want the algorithm to account for cause not correlated patterns that reinforce racial divisions and then perpetuate the inequality.
It’s not because of the color of someone’s skin. It’s not even clear where racial lines are drawn and previously distinct “races” have disappeared into the general “Caucasian” label depending on time period.
Your comments are why it’s a problem - people are unable to understand the difference and overfit to patterns in the data. Our learning shares a similar problem to ML.
When you don’t correct for this it hurts people of that race and helps to perpetuate the imbalance - reinenforcing the historical data and increasing inequality.