Which is why I'm surprised about all this "AI is biased" outrage. A decent algorithm will learn what's in the data. Cast on a wide enough scale, the data is roughly what the world is. If your bot learns from newspaper corpus, then it learns how the world looks through the lens of news publishing. If news publishing is somewhat racist, and your algorithm does not pick on that, then your algorithm has a bug in it.
It seems to me like the people writing about how AI is bad because it picks up biases from data are wishing the ML would learn the world as it ought to be. But that's wrong, and that would make such algorithms not useful. ML is meant to learn the world as it is. Which is, as you wrote, neither fair nor a controlled experiment.
Well put. The people complaining about how AI is bad are the same people who push "diversity hires" to try to pretend that the population of software developers is equal parts male/female, and white/black.
It seems to me like the people writing about how AI is bad because it picks up biases from data are wishing the ML would learn the world as it ought to be. But that's wrong, and that would make such algorithms not useful. ML is meant to learn the world as it is. Which is, as you wrote, neither fair nor a controlled experiment.