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This is, incidentally, the same basic reason why so many knee-jerk policies produce systems that can never do anything but fail. When trying to find needles in
by reincarnate0x14 5y ago
This is, incidentally, the same basic reason why so many knee-jerk policies produce systems that can never do anything but fail. When trying to find needles in hay stacks, since the needle (let's say terrorists trying to board airplanes) is so tiny relative to the hay (the millions of other people doing same), the people responsible for reacting are essentially trained only on false positives and still miss the real events when they (highly infrequently) occur.
Obviously with low-impact false positives like requiring more expensive or invasive, but not really dangerous, additional medical tests this is an easily accepted and accounted for set of outcomes, but as we try to do things like apply ML to ever more specific cases of complex questions it's worth keeping in mind.
- deleted 5y ago[deleted]
- majormajor 5y agoGoing a level further, the claim that the systems fail is itself tough to support! Let's say two million people have been seriously inconvenienced by airport screening. We have to ask if that's an acceptable price to pay for the benefits? But do we know the benefits? Can we know the benefits? We actually don't know if the policy deterred anything, or even if someone who wanted to do something bad had their knife or liquid explosive or whatever confiscated and just abandoned the plan. It's purely a hypothetical so you're going to support the view you want: "we don't need to do all this because we don't actually have a big threat" or "we have successfully prevented any more attacks." After all, we often see discussions here of foolish companies that underfund their security teams because "why are we paying so much for this when nobody has cyberattacked us?" ;)
- potatoman22 5y agoFalse positives lead to poor use of resources