Consider the following situation: There are two groups, A and B. For both groups, when a person is labeled high risk they have a 75% risk of reoffending (ie no

Consider the following situation: There are two groups, A and B. For both groups, when a person is labeled high risk they have a 75% risk of reoffending (ie no bias). 10% of Group A people are labeled high risk. Which means that (1 - 75%) * 10% = 2.5% are misclassified. 20% of Group B people are labeled high risk. Which means that (1 - 75%) * 20% = 5% are misclassified. As you can see, this is simply an artifact of the higher reoffending rate of Group B. If 1) the two groups have different reoffending rates, and 2) the algorithm for labeling people high risk is unbiased, it necessarily follows that the two groups will have different misclassification rates. The entire ProPublica piece (and your arguments) is based on misunderstanding this. Part of the issue is that extremely misleading table titled "Prediction Fails Differently for Black Defendants", where "Labeled Higher Risk, But Didn’t Re-Offend" actually refers to P(labeled higher risk | didn't re-offend).