Plus, ML will allow us to classify and optimize at scale, and be better at it than humans potentially, but opaquely... Humans hire from alumni network, have gender/race biases in hiring and are credentialist. What is ML going to weed out? Don't even know where to begin to look.+
So that I'm clear: is the assertion you are objecting to this:"ML is different than trad programs or databases and that it creates (for computation) unique challenges to transparency and auditing?" It's good for me to understand because, honestly, I'd have ranked that as mundane.
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(Also note that I'd group ML with human decision-making when it comes to transparency/auditing challenges, and I'd group bureaucracy, formalized decision making and symbolic/traditional programming together.)
Thanks. Twitter will use this to make your timeline better. UndoUndo
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That’s a truism. The problem is that you can also say this about any new technology. Everything is always different in some important way, and so, really, it’s not a new problem at all. There are now a dozen or two replies to you pointing this out in various ways.

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I actually don't think so, but thanks for the clarification. Yes, I'm asserting that there is something qualitatively different about ML than any other new technology—it's not just that it's opaque (or seemingly-magical) to non-experts. It's intrinsically opaque to its experts.+
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It's just not the same as say, adding more variables like heart rate, blood pressure, this and that measurement and running a regression or applying a formula.