These are fundamentally institutional problems, not technology problems. If the IRS misspells your name and you can’t get anyone to correct it, is that Oracle’s fault? Or SQL’s? Or is it the fault of a bureaucracy that hasn’t built the right processes?
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Those two have always been intertwined! Besides, even the simplest database errors are experienced differently by the less powerful. I hear a lot of insane stories. Also these technologies can be empowering to the empowered and controlling to those who are not—especially at work.
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Replying to @zeynep @benedictevans and
Machine learning is going to mean a very different thing at lower ranks of the employment hierarchy.
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Or, ML algorithms will, without anyone understanding what's going on, weed out people prone to depression or women more likely to become pregnant in the next two years. It's ML, not just bureacratic rules codified into some code.
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Why is an institution implementing an ML system without understanding how it could be wrong any different to an institution implementing a database without having that understanding?
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Cannot even reverse/engineer debug. I think a better grouping is that ML is opaque like humans, but as humans we have some insight into human foibles. Traditional databases (or programs) are like bureaucratic rules: they can be a maze, but you can potentially figure them out. 1/2
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The whole point of unconscious bias is that problems can be in plain sight but people still can’t see them. The Guardian’s inclusion group set up an unpaid internship program and never once thought who that excluded. That’s not an auditability problem. It’s an awareness problem.
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Sounds like something a well-designed ML algorithm could surface.
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Humans have both unawareness problem and inauditability problem, but of course we are going to react differently to human failings for which we have so much culture, institutions, focus and plain insight from being same species. I'm not arguing humans are great, but they're us.
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