> For example, it is a common belief within the medical community that Black people have higher pain tolerance, and thus don't need as much or as strong of pain killers. People in pain sitting in beds in the ER will be delayed or refused pain killers because of this. This belief has been proven false, but it shows up in papers, notes, etc. and nurses and doctors still will treat their patients as if it's true.

Nurses and doctors don't refuse giving painkillers because they enjoy seeing their patients suffer, painkillers come with side effects. So they are trying to find a balance between preventing suffering and long term health. We can look at the wrong idea that black people have a higher pain tolerance as causing black patients unnecessary suffering, but you can also see it as protecting the long term health of black patients more. That's the same thing, just seen from a different angle.

But now that we know of this bias, we can of course try to fix it, but how. Now that we also have data: do black people have less painkiller-related problems? If they do, maybe we should give the black treatment to others, if they don't maybe they could get some more.

Maybe. I'm not so sure. If you change skin color, it might identify race by other aspects. If you stage the same situation with an entirely different person or otherwise digitally edit it, it might detect in some way that it's not a real situation or an altered image and respond based on that. They're black boxes.

I remember someone talking about AI detecting lung cancer from MRIs, and they discovered that it was assigning higher probabilities based on the age of the machine that took it (or maybe fMRI, and maybe it wasn't lung cancer, I'm forgetting the details). So those communities were poorer and had worse health and a higher incidence, or they'd have had the newer machine. You never really know how it's deciding anything (though in that case they did figure it out, it might not be impossible, but in the end, you probably end up figuring it out at a statistical level--I suspect the counter-factuals wouldn't be reliable), so you're introducing variables attempting to make a counter-factual, which makes me think that in the end, it'll come down to statistical analysis from real data/people/situations. Which is after you've let it loose doing the thing, unless you run it in parallel for a while before relying on it at all and statistically analyze that before relying on it. I'm not sure you can isolate a variable sufficiently except by statistical means on real data, because it looks at everything, and it might find a correlate to your correlate, and a correlate to that correlate, and reach the same conclusion based on your counter-factual not being able to sufficiently hide what it was looking at

I don't know, random thoughts

potato, potahto

Except crime occurs in pretty constant amounts over large populations. What really sets minority neighborhoods apart is far more aggressive policing, which finds more crime to be prosecuted for. Which is then used as both evidence backing racist claims by politicians, and reason to increase patrols further, which then find more crime, and so it continues.

It's also worth noting that numerous crimes, the most egregious examples probably being cocaine/crack and marijuana, are explicitly racialized in their enforcement. Cocaine in rich white suburbs is a health issue. Crack in the ghetto is a crime wave. It's literally the same substance, the only difference is how it looks and who is taking it. (and if it was possibly supplied by the CIA...)

Meanwhile white offenders of pot abuse get off with a few months here and there, and black people caught with it frequently, you know, fucking die.

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