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this post was submitted on 08 Mar 2025
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That's not what the article is about. I think putting some more objectivety into the decisions you listed for example benefits the majority. Human factors will lean toward minority factions consisting of people of wealth, power, similar race, how "nice" they might be or how many vocal advocates they might have. This paper just states that current AIs aren't very good at what we would call moral judgment.
It seems like algorithms would be the most objective way to do this, but I could see AI contributing by maybe looking for more complicated outcome trends. Ie. Hey, it looks like people with this gene mutation with chronically uncontrolled hypertension tend to live less than 5years after cardiac transplant - consider weighing your existing algorithm by 0.5%
Creatinin in urine was used as a measure of kidney function for literal decades despite African Americans having lower levels despite worse kidneys by other factors. Creatinine level is/was a primary determinant of transplant eligibility. Only a few years ago some hospitals have started to use inulin which is a more race and gender neutral measurement of kidney function.
No algorithm matters if the input isn't comprehensive enough and cost effective biological testing is not.
Well yes. Garbage in garbage out of course.
That's my point, this is real world data, its all garbage, and no amount of LLM rehashing fixes that.
Sure. The goal is more perfect here, not perfect.