Seems a bit different from modern day algorithmic discrimination. In the 2016 pro publica example, for instance, the algorithm doesn't explicitly consider race

Seems a bit different from modern day algorithmic discrimination. In the 2016 pro publica example, for instance, the algorithm doesn't explicitly consider race but just picks up the fact that black inmates more often have other features correlated with reoffending (they are younger, for instance). Now that might very well indicate that there's a systematic bias against younger black people in the training data and that they are more likely to be singled out for arrest than others, but the algorithm did an okay job given the unbalanced arrest prevalence in the training data. Of course, there's still a lesson of not blindly trusting the algorithm but trying to understand why the data caused it to make its decisions. But at least no one went to such lengths as to encode ethnicity as a separate feature to weigh in the process. I wish the article gave more detail about the algorithm - was it a manually constructed decision tree or what? Because I wonder what the guy was doing. Surely even in the 70/80s you should have been aware that encoding ethnicity as some explicit variable is very discriminatory.