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Comment by kennywinker | Hacker News Reader

There is still a categorical difference between how they are being used. Specifically analytic vs generative. Generative AI (LLMs and image generators) are the ones people have issues with - pretty much nobody cares about ML processing for analysis.

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There’s a bit of a grey area, for example speech recognition. Would you classify that as analytic or generative? Whisper and speech LLMs work pretty well, but can completely make up stuff that wasn’t in the audio at all (see e.g. “thank you for watching” transcribed during silence). Other approaches are closer to the acoustic evidence but may make other mistakes (especially wrongly transcribing long tail, low frequency terms). Pick your poison.

Analytic, but badly made.

> pretty much nobody cares about ML processing for analysis.

I work in a bank and a can tell you that the customers absolutely hate ML when it rejects their loan application. Over the pond in the US, I have an impression that the fico score is not exactly popular either, but I have no first hand experience.

As long as you can get a reason from the model, it's not that bad.

Black box automatic decision making is much more problematic.

In the US, FICO scores are mainly unpopular with "credit criminals" who have low scores. The score performs quite well in predicting how likely a borrower is to repay a loan. The problems that arise with credit scores in general are when they're used for purposes they were never designed to serve, like screening job applicants.

For home mortgage lending, FICO scores are now being partially replaced by VantageScore.

https://vantagescore.com/

People have absolutely hated their loan applications being rejected since before ML was being used anywhere near it.

As evidence, let me cite the “computer says no.” sketch from 2004

In 2004, the logic driving such decisions was literally GOFAI: rules engines that fire off responses when certain criteria are met.

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