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My understanding is that the tumor detecting machine vision is generally thought useful in addition to the radiologist’s expertise. It basically outputs “yes”, “maybe”, and “no”, which is more expertise respecting than generating somewhere thereabouts code, which the coder has to (now) validate.

This is why I wouldn’t equate these tools. LLM code generation is marketed to do much more than machine vision for tumor detection.

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AnarchistArtificer@slrpnk.net ⁨5⁩ ⁨days⁩ ago
Cory Doctorow actually goes more in depth on the radiologist example in a post from last year:

‘If my Kaiser hospital bought some AI radiology tools and told its radiologists: “Hey folks, here’s the deal. Today, you’re processing about 100 x-rays per day. From now on, we’re going to get an instantaneous second opinion from the AI, and if the AI thinks you’ve missed a tumor, we want you to go back and have another look, even if that means you’re only processing 98 x-rays per day. That’s fine, we just care about finding all those tumors.”

If that’s what they said, I’d be delighted. But no one is investing hundreds of billions in AI companies because they think AI will make radiology more expensive, not even if that also makes radiology more accurate. The market’s bet on AI is that an AI salesman will visit the CEO of Kaiser and make this pitch: "Look, you fire 9/10s of your radiologists, saving $20m/year, you give us $10m/year, and you net $10m/year, and the remaining radiologists’ job will be to oversee the diagnoses the AI makes at superhuman speed, and somehow remain vigilant as they do so, despite the fact that the AI is usually right, except when it’s catastrophically wrong.

“And if the AI misses a tumor, this will be the human radiologist’s fault, because they are the ‘human in the loop.’ It’s their signature on the diagnosis.”

This is a reverse centaur, and it’s a specific kind of reverse-centaur: it’s what Dan Davies calls an “accountability sink.” The radiologist’s job isn’t really to oversee the AI’s work, it’s to take the blame for the AI’s mistakes.’

In short, we definitely could (and indeed should) be using tools like tumor detecting machine vision as something that helps humans build a better world for humans. But we’ve seen time and time again, across countless fields that it never works out that way.

That’s because this isn’t a problem with the technology of AI, but the fucked up sociotechnical and economic systems that govern how this tech is used, who gets to use it, who it gets used on, whose consent is required for those uses and most significant of all: who gets to profit?

!Not us, that’s for sure!<

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