this post was submitted on 07 Aug 2023
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From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models https://aclanthology.org/2023.acl-long.656.pdf

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[–] [email protected] 16 points 1 year ago (8 children)

It's the end result of training your AI on mountains of biased human thoughts

[–] [email protected] 2 points 1 year ago (4 children)

Not trying to be a smartass, but what's the alternative?

[–] [email protected] 8 points 1 year ago

Does there have to be one? It'd be nice if there were, of course, but this is currently the only way we know of to make these AIs.

[–] [email protected] 5 points 1 year ago (1 children)

Well, you can focus on rule-based/expert system style AI, a la WolframAlpha. Actually build algorithms to answer questions that are based on scientific fact and theory, rather than an approximated consensus of many sources of dubious origin.

[–] [email protected] 3 points 1 year ago

Ooo, old school AI 😍

In our current cultural consciousness, I'm not sure that even qualifies as AI anymore. It's all about neutral networks and machine learning nowadays.

[–] [email protected] 4 points 1 year ago (1 children)

I guess shoving an encyclopedia into it. I'm not sure really, it is a good point. Perhaps AI bias is as inevitable as human bias...

[–] [email protected] 8 points 1 year ago (1 children)

Despite what you might assume, an encyclopedia wouldn't be free from bias. It might not be as biased as, say, getting your training data from a dump of 4chan, but it'd absolutely still have bias. As an on-the-nose example, think about the definition of homosexuality; training on an older encyclopedia would mean the AI now thinks homosexuality is a crime.

[–] [email protected] 4 points 1 year ago

And imagine how badly most encyclopedias would reflect on languages and cultures other than the one that made them.

[–] [email protected] 2 points 1 year ago

The alternative is being extremely careful about what data you allow the LLM to learn from. Then it would have your bias, but hopefully that'll be a less flagrantly racist bias.

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