[1] See the 1999 documentary Blast from the Past.
But I think we're diverging considerably from the original point: that forcing an AI to produce equal outcomes despite unequal behavior in the real world is not the elimination of bias, it's the deliberate introduction of bias. If we have an AI that predicts recidivism rates, and we engineer it to produce equal predicted rates across all groups despite different between rates groups in the real world then we are deliberate introducing bias. The truth, regrettable thought may be, is that a magical AI that operates with 100% accuracy - the only people who it flags would have re-offended - is going to produce disparities because recidivism rates are not equal.
The blindspots end up being extremely problematic.
The justice system should absolutely not use this system for any purposes, since justice is based on the circumstances of the individual case in front of it, not the societal statistics which apply in the aggregate but may not apply to that specific case.
And if an activist engineer deliberately biased the model to avoid indicating disparities in crime, then we will have sabotaged police's abilities to allocated resources. Hence, why this assumption that disparate outcomes are indicative of a biased model is a problem
We already know where crime is occurring. We don't need an AI model for that.
People aren't arguing about not using biased data, they're arguing that the model needs to be designed and trained so that the bias in the data doesn't affect the predictions in the model. And yes, that means deliberately de-engineering bias out of the model, which may involve introducing a counter-bias.
For example, you and others kept bringing up race earlier as a legitimate bias for criminal profiling. But socioeconomic status is far more correlated with propensity to commit criminal acts than race. A model of crime in LA based on race, for example, would assume that people in Ladera Heights are just as likely to commit crime as people in South LA because they have the same race...but Ladera Heights has a fraction of the crime as South LA (and several times the average income). Similarly, you would expect South LA to have less crime than the largely Caucasian Joshua Tree or Fontana...but both cities have higher crime rates than South LA, and for a period were some of the most dangerous cities in California. (Joshua Tree was the inspiration for, and original setting of, Breaking Bad. Fontana used to be known as the Felony Flats.)
What I am saying is that pointing to disparities in the outcome of these models to claim that the models are biased is not, on it's own, a reasonable conclusion. As you point out, people in Joshua Tree and South LA have higher rates of crime than average. So if our model flags people from these areas as high risk more frequently than other places, is our algorithm biased? If we deliberately make the model to produce uniform results across different locations because an engineer feels that it's problematic to have a model that produces different results between different geographic cohorts, then have we mitigated bias? No, that engineer intentionally introduced our own bias to make the model adhere to his or her worldview.