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submitted 11 hours ago by Gaywallet@beehaw.org to c/technology@beehaw.org
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[-] t3rmit3@beehaw.org 34 points 11 hours ago

Santa Clara County alone has 24 million property records, but the study team focused mostly on 5.2 million records from the period 1902 to 1980. The artificial intelligence model completed its review of those records in six days for $258, according to the Stanford study. A manual review would have taken five years at a cost of more than $1.4 million, the study estimated.

This is an awesome use of an LLM. Talk about the cost savings of automation, especially when the alternative was the reviews just not getting done.

[-] knightly@pawb.social 5 points 11 hours ago

Given the error rate of LLMs, it seems more like they wasted $258 and a week that could have been spent on a human review.

[-] OmnipotentEntity@beehaw.org 14 points 7 hours ago

LLMs are bad for the uses they've been recently pushed for, yes. But this is legitimately a very good use of them. This is natural language processing, within a narrow scope with a specific intention. This is exactly what it can be good at. Even if does have a high false negative rate, that's still thousands and thousands of true positive cases that were addressed quickly and cheaply, and that a human auditor no longer needs to touch.

[-] t3rmit3@beehaw.org 14 points 10 hours ago* (last edited 10 hours ago)

What do you believe would make this particular use prone to errors?

[-] knightly@pawb.social 1 points 1 hour ago

The use of LLMs instead of someone that can actually understand context.

[-] GetOffMyLan@programming.dev 1 points 4 minutes ago

One of LLMs main strengths over traditional text analysis tools is the ability to "understand" context.

They are bad at generating factual responses. They are amazing at analysing text.

[-] t3rmit3@beehaw.org 2 points 1 hour ago

I think you may have misunderstood the purpose of this tool.

It doesn't read the deeds, make a decision, and submit them for termination all on its own. It reads them, identifies racial covenants based on patterns of language (which is exactly what LLMs are very good at), and then flags them for a human to review.

This tool is not replacing jobs, because the whole point is that these reviews were never going to get the budget and manpower to be done manually, and instead would have simply remained on the books.

I get being disdainful or even angry about LLMs in our unregulated-capitalism anti-worker hellhole because of the way that most companies are using them, but tools aren't themselves good or bad, they're just tools. And using a tool to identify racial covenants in legal documents that otherwise would go un-remediated, seems like a pretty good use to me.

[-] knightly@pawb.social 1 points 1 minute ago

So, what? They're going to pay a human to OK the output and the whole lot of them never even gets seen?

Say 12 minutes per covenant, that's 1 million work hours that humans could get paid for. Pay them $50 an hour and it's $50 million. That's nothing, less than 36 hours worth of the $12.5 Billion in weapons shipments we've sent to Israel in the last year. We could pay for projects like this with the rounding errors on the budget for blowing up foreign kids, and the people we pay to do it could afford to put their kids through college.

Instead, we get a project to train a robotic bigotry filter for real estate legalese and 50 more cruise missiles from the savings.

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this post was submitted on 21 Oct 2024
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