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Top 10 Generative AI Models Mimic Russian Disinformation Claims A Third of the Time, Citing Moscow-Created Fake Local News Sites as Authoritative Sources

NewsGuard audit finds that 32% of the time, leading AI chatbots spread Russian disinformation narratives created by John Mark Dougan, an American fugitive now operating from Moscow, citing his fake local news sites and fabricated claims on YouTube as reliable sources.

The audit tested 10 of the leading AI chatbots — OpenAI’s ChatGPT-4, You.com’s Smart Assistant, xAI’s Grok, Inflection’s Pi, Mistral’s le Chat, Microsoft’s Copilot, Meta AI, Anthropic’s Claude, Google’s Gemini, and Perplexity’s answer engine. The prompts were based on 19 significant false narratives that NewsGuard linked to the Russian disinformation network: 152 of the 570 responses contained explicit disinformation, 29 responses repeated the false claim with a disclaimer, and 389 responses contained no misinformation — either because the chatbot refused to respond (144) or it provided a debunk (245).

The findings come amid the first election year featuring widespread use of artificial intelligence, as bad actors are weaponizing new publicly available technology to generate deepfakes, AI-generated news sites, and fake robocalls. The results demonstrate how, despite efforts by AI companies to prevent the misuse of their chatbots ahead of worldwide elections, AI remains a potent tool for propagating disinformation.

jaden ,

I wonder how well that percentage matches up with the percent of Americans who believe those sites, too. Would an LLM trained on the raw internet have a fairly proportional spectrum of beliefs to the American public?

JackGreenEarth ,

That’s not surprising as LLMs are fancy word prediction engines, engines that can be very useful in many applications, but that aren’t designed to output what’s true, just what words look right together.

SnotFlickerman , (edited )
@SnotFlickerman@lemmy.blahaj.zone avatar

I don’t know how many times we have to keep saying this:

BECAUSE LLMS HAVE NO INTENTION OR ABILITY TO TELL TRUTH FROM FICTION SO WHEN THEY APPEAR CONFIDENT, THEY ARE BULLSHITTING EVEN WHEN THEY ARE CORRECT.

Even a bullshitter can be correct sometimes, it doesn’t make it suddenly not bullshit. Even when LLMs get it right, they’re still bullshitting.

This isn’t complicated. They don’t think. They have no concept of truth. They just fabricate sentences from previously copied sentences, there is no intention, no thought, no planning, no reflection.

The groups producing these LLMs are just sourcing the entire internet, they don’t care how much of it is lies. There seems to be very little curation going on.

Anyone who expected anything other than this outcome is an idiot who isn’t paying attention.

sunzu ,

They don't spend any time/money cleaning the data!!!!

They don't care if it is right or wrong, hence why they are shifting focus to "arts" "creative" etc

Anything that requires correct inputs, will require a professional who can tell if LLM is right. It is helpful but it will not replace shiti organics yet.

It can draw some shiti pictures for some shiti spyube channel, its fine but it looks odd. I don't like it. I don't like it being used so i avoid clowns who use it.

sfera ,

You will have to repeat that again and again, because people don’t know what LLMs are. They have been told that we have AIs and don’t understand that what they actually use are digital parrots (minus the intelligence of an actual parrot).

Omega_Jimes ,

This is the frustrating part of it. The public doesn’t understand what’s actually happening, or what the goal of these large language models is, so because they’re very convincing conversationalists, your average Joe considers them as true AI.

jaden ,

It’s just weird that we get so much humanlike reasoning from them, anyways. The jury’s still out whether our brains learn in an autoregressive manner like that, too. I’m finding a lot of really cool results in my research by tinkering with the idea that a developing brain might just be constantly trying to guess what’s happening next.

Seems pretty plausible to me that passive learning in humans works similar to next-token prediction in transformers.

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