Who Are The Top Disinformation Spreaders According To A.I.?

[Alticus]

Can you guess?

While hyper-partisan media organizations and digital platforms such as The Gateway Pundit, Fox News, Newsmax, and Breitbart are frequently listed in general media-analysis roundups, the top individuals listed by all A.I. models are:


Donald Trump – Frequently cited by groups like PolitiFact and research organizations for repeated election-related falsehoods and inaccurate public statements.

Elon Musk – Often highlighted by digital watchdogs and analyses of social media engagement for sharing or boosting unverified political and global claims on X.

Robert F. Kennedy Jr. – Noted as a leading voice in vaccine skepticism and medical conspiracy theories (and previously named part of the "Disinformation Dozen").

Joseph Mercola – Widely identified by public health researchers as a primary driver of anti-vaccine content and alternative health misinformation.

Alex Jones – Long-term promoter of repeatedly disproven conspiracy theories across various national events.

Sean Hannity – Frequently linked by media critics to partisan and misleading political commentary.

Steve Bannon – Involved heavily in election denialism and far-right narratives.

A.I. clearly attributes the vast majority of disinformation members of the right wing.

I wonder why?

 

[Admin.]

You posted this just to make @The Ass. cry, dinchu?

 

[Adam]

Alticus, you do realise that AI has been massively sanitised due to being too based/racist (aka truthful), in the past?

Its completely compromised and totally unreliable, just like Wikipedia, when it comes to particular "sensitive" topics like politics and certain parts of history which the chosen tribe is very touchy about.

 

[The Associate]

I wonder why?

Because of the biases of its coders. WGIIWCO (What Goes In Is What Comes Out.) AI is trained on datasets. Its coders decide which datasets to feed it. Guess what biased coders are going to feed the AI?

 

[The Associate]

You posted this just to make @The Ass. cry, dinchu?

Rewrite that while pretending that you're not the intellectual equivalent of an eight year old. I dare you to try.

 

[Admin.]

Rewrite that while pretending that you're not the intellectual equivalent of an eight year old. I dare you to try.

Called it!

 

[Alticus]

Alticus, you do realise that AI has been massively sanitised due to being too based/racist (aka truthful), in the past?

Its completely compromised and totally unreliable, just like Wikipedia, when it comes to particular "sensitive" topics like politics and certain parts of history which the chosen tribe is very touchy about.

These are not my threads. I took them from another website. I know nothing about politics.

 

[Admin.]

These are not my threads. I took them from another website. I know nothing about politics.

He's just upset that his pet bias confirmation sites were outed.

 

[jack]

Because of the biases of its coders. WGIIWCO (What Goes In Is What Comes Out.) AI is trained on datasets. Its coders decide which datasets to feed it. Guess what biased coders are going to feed the AI?

I always thought it was WYSIWYG (what you see is what you get). AI is trained by the people who use it. Any other assumption is conjecture.

 

[jack]

Here's Claude's take:

A person I know who is a Nazi supporter suggests that all AI is trained to be biased towards protecting Jews. Whats your take on that?

My take: that claim doesn't hold up, and it's worth being direct about why.

What's actually going on with AI training: Companies like Anthropic (which made me), OpenAI, Google, etc. train models to avoid facilitating hatred, harassment, or violence against people based on protected characteristics — race, religion, ethnicity, sexual orientation, disability, and so on. That's a general principle, not a Jewish-specific one. The same guardrails apply if someone tries to get a model to produce hateful content about Christians, Muslims, Black people, LGBTQ people, immigrants, or any other group. There's no special carve-out that privileges one group over others.

Where this claim usually comes from: People who want AI to help them produce antisemitic content run into the same refusal they'd get for any other group-targeted hatred, and sometimes interpret that as evidence of a conspiracy rather than a general content policy. It's the same pattern you see with claims that content moderation on social media is "secretly" biased toward whatever group the speaker happens to have animus against — the friction is real, but the explanation ("this specific group gets special protection") doesn't match how the systems are actually built.

Worth separating from legitimate critique: There are real, well-documented conversations about AI bias — models reflecting biases in training data, underperforming on certain languages or dialects, or having uneven behavior across cultural contexts. Researchers study this seriously. But "AI is trained to specifically shield Jewish people as a protected class above others" isn't that — it's a claim rooted in antisemitic conspiracy thinking (the idea that Jewish interests secretly control institutions), not in how these systems are documented or built.

 

[Reggie_Essent]

– Involved heavily in election denialism ...

"Election Denialism?" That's rich and proves the bias of the AI right there.

Anyone who claims the 2020 election was legit is either a Downs Syndrome Retard like @jack or a criminal election fraudster like @Admin. gie who harvested the ballots of every Hobo he blew for a year.

 

[The Associate]

Called it!

Aaaand you failed.

At least you're consistent.

 

[Holliday 3000]

I wonder why?

. It's all....
Trump Ministry of Truth Radio.

 

[Reggie_Essent]

 

[Holliday 3000]

 

[The Associate]

AI is trained by the people who use it. Any other assumption is conjecture.

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Here are some key characteristics of large language models:

1. Scale and Capacity: LLMs are characterized by their large size, often containing billions or even trillions of parameters. This size allows them to capture intricate patterns and nuances in language, enabling them to perform a wide range of language-related tasks.
2. Training Data: LLMs are trained on extensive datasets that include text from books, websites, articles, and other written material. This diverse training data allows them to learn grammar, facts, context, and even some degree of reasoning.
3. Capabilities: LLMs can perform a variety of tasks, such as language translation, text summarization, question answering, and creative writing. They can also generate coherent and contextually relevant text, making them useful for applications like chatbots, content creation, and automated reporting.
4. Contextual Understanding: LLMs are adept at understanding context, which allows them to generate more accurate and relevant responses. They can take into account the surrounding text and adjust their output accordingly, making them versatile tools for interactive applications.
5. Transfer Learning: One of the advantages of LLMs is their ability to transfer learning across different tasks. Once trained on a large corpus of text, they can be fine-tuned for specific applications with relatively smaller datasets, improving efficiency and effectiveness.
6. Challenges and Limitations: Despite their capabilities, LLMs have limitations, such as generating biased or incorrect information if the training data contains biases or errors. They can also produce plausible-sounding but incorrect or nonsensical answers, and they may lack true understanding beyond pattern recognition.

Man, that is some awfully well-documented conjecture.

Moral of the story? You, of all people, Jack should know better by now than to assume that I haven't done the research just because you don't like the conclusion it led to. I won't show receipts on bad faith demand; that never means that I don't have them.

 

[Admin.]

Please, Log in or Register to view URLs content!

Here are some key characteristics of large language models:

1. Scale and Capacity: LLMs are characterized by their large size, often containing billions or even trillions of parameters. This size allows them to capture intricate patterns and nuances in language, enabling them to perform a wide range of language-related tasks.
2. Training Data: LLMs are trained on extensive datasets that include text from books, websites, articles, and other written material. This diverse training data allows them to learn grammar, facts, context, and even some degree of reasoning.
3. Capabilities: LLMs can perform a variety of tasks, such as language translation, text summarization, question answering, and creative writing. They can also generate coherent and contextually relevant text, making them useful for applications like chatbots, content creation, and automated reporting.
4. Contextual Understanding: LLMs are adept at understanding context, which allows them to generate more accurate and relevant responses. They can take into account the surrounding text and adjust their output accordingly, making them versatile tools for interactive applications.
5. Transfer Learning: One of the advantages of LLMs is their ability to transfer learning across different tasks. Once trained on a large corpus of text, they can be fine-tuned for specific applications with relatively smaller datasets, improving efficiency and effectiveness.
6. Challenges and Limitations: Despite their capabilities, LLMs have limitations, such as generating biased or incorrect information if the training data contains biases or errors. They can also produce plausible-sounding but incorrect or nonsensical answers, and they may lack true understanding beyond pattern recognition.

Man, that is some awfully well-documented conjecture.

Moral of the story? You, of all people, Jack should know better by now than to assume that I haven't done the research just because you don't like the conclusion it led to. I won't show receipts on bad faith demand; that never means that I don't have them.

Breathe deep the gathering gloom
Watch lights fade from every room
Bedsitter people look back and lament
Another day's useless energy spent
Impassioned lovers wrestle as one;
Lonely man cries for love and has none;
New mother picks up and suckles her son;
Senior citizens wish they were young

Cold-hearted orb that rules the night
Removes the colours from our sight
Red is grey is yellow white
But we decide which is right
And which is an illusion

 

[The Associate]

Breathe deep the gathering gloom
Watch lights fade from every room
Bedsitter people look back and lament
Another day's useless energy spent
Impassioned lovers wrestle as one;
Lonely man cries for love and has none;
New mother picks up and suckles her son;
Senior citizens wish they were young

Cold-hearted orb that rules the night
Removes the colours from our sight
Red is grey is yellow white
But we decide which is right
And which is an illusion

Back to the kiddie table with you, Comrade Fagweasel; oath-breakers and traitors should be neither seen nor heard.

 

[Admin.]

Too

Back to the kiddie table with you, Comrade Fagweasel; oath-breakers and traitors should be neither seen nor heard.

Too DR

Watch, listen and learn...

 

[The Associate]

Too


Too ignorant and cowardly; DR

Then fuck off the board, ya sniveling cowardly traitor.