“Money laundering” is taking criminal money (maybe marked currency or something) and trading them out for “clean” currency that can’t be tracked.
Imagine a dataset about healthcare outcomes. You want to know whether airlifting a patient is good or bad, so you train a machine learning model to predict mortality given airlifting a patient versus not airlifting them. Turns out a lot more of the airlifted patients died, and the model picks up on that, deciding that airlifting is dangerous.
Obviously, we’re airlifting the patients who are in the most dire circumstances, and that’s why they die more, but maybe the model doesn’t have the context/circumstance variables to see that, or maybe it’s just regularized and thinks those context variables are noise. So the bias of “airlift = bad” gets stuck in the model, but then people defend it as mathematically precise so it can’t be biased like a person can. They say that the model just reflects reality, pretending the model or data is wrong is blindly rejecting reality for the sake of political correctness.
It’s worse with really human stuff like recidivism because the context variables that might be useful (like the patient’s dire circumstances) tend to be very human and complex and they’re unlikely to be captured in a simple form. Even if they were, interpreting them might require human level AI. By analogy, it is frequently impossible (or just too hard to be worth it) for current technology’s ML to tell from the data we feed it that the patients being airlifted were most near death to begin with.
So you end up with an algorithm biased against airlifting patients getting defended as mathematically bulletproof.