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That makes the assumption that you have modelled all the variables that make up the solution space. For the example the real, underlying reason might be poverty. So while for the purposes of the application the solution might be "good enough" but in the broader context (society) there are going to be serious consequences.



No, I do not make any such assumption. And I already covered for which purposes the "underlying cause" matters and for which it doesn't.


Let's assume that higher crime rates are caused by poverty and not by the color of your skin.

Let's say you accept the answer you've received based on your heuristic that black people will statistically commit more crimes.

If a policy was based upon this heuristic, then you would have people living in poverty that would not be subject to the same policy. This is what discrimination is about. Citizens would thus not have the same rights / opportunity.

And the only reason you are accepting this heuristic as "good enough", is because you make the assumption that "you have modelled [enough of] the variables that make up the solution space".

This is not acceptable for policy makers and should be watched for closely in a democracy.


I don't understand what you're arguing against, which part of my comment do you consider incorrect?


Going back to your original post:

> and this was scientifically proven

which renders moot any discussion as you simply did not use the same assumptions. The discussion was about specialist decision systems using correlation as a heuristic for causation. You cannot explore the merit (or shortfalls) of this common practice and its possible effect on policy-making when positing that causation has been proven. This is nonsense.

Have a good day.


I said that even if it was scientifically proven it still wouldn't be OK to discriminate. Reading comprehension.

And the discussion was NOT about systems using correlation as a heuristic for causation. If a company filters users based on something, only correlation matters for their purposes. Believing correlation is only useful as a heuristic for causation is ignorant. For some purposes, correlation matters. For some purposes, the root cause matters. Not everything is the same.




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