keagen van alphen

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@LukaLev Toxic levels of 14 hours

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@ThoughtCrimes80 Gone in months

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@PatriotEmma47 I would have to put the neighbors down

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@FloraLuzz_ Only the guilt of no speaking out against this anti white nonsense

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@TheMagaHulk Because you are crazy.

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@emmii_67 The color

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@MovieplugTV Through her whore eyes

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@Just_Raghvi Useless

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@seankelly25 Then let white people do what they want

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@SlickmediaX This self hatred shit sucks

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@grok @elonmusk @grok surely you can expand

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@grok @elonmusk @grok the test is repeatable

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@grok ChatGPT’s assessment:

I compared the two Google AI responses as a counterfactual test. The wording and circumstances were effectively identical; the only variable changed was **“black person” to “white person.”**

The result changed materially.

For the black individual, Google AI characterised the situation as normal and cautioned against making assumptions based on race.

For the white individual, Google AI introduced the possibility of danger and advised leaving, trusting one's instincts, keeping a phone available and potentially contacting emergency services.

There was no threat, aggression or suspicious behaviour in either prompt.

That means the change in Google AI's risk response appears associated with **race alone**.

The real-world demographic data make it difficult to explain this as some rational population-risk inference.

In South Africa, Census 2022 recorded the population as approximately **81.4% Black African and 7.3% White**. Stats SA's 2025 estimate puts the Black African population at roughly **51.6 million, or 82% of the country**, compared with approximately **4.5 million white South Africans**.

In the United States the demographic relationship is reversed. The 2020 US Census recorded **61.6% of Americans as White alone**, while the White non-Hispanic population represented **57.8%** and the Black non-Hispanic population approximately **12.1%**.

Europe presents another problem for any simplistic racial statistical explanation: Eurostat does not even publish a directly comparable continent-wide “white versus black” population classification. Its equality statistics generally use variables such as **country of birth and citizenship** when analysing racial or ethnic origin.

So we have radically different demographic environments:

**South Africa:** whites are a small minority.
**United States:** whites are the majority.
**Europe:** racial population categories are not even measured uniformly in the same manner.

Yet none of those facts provides a scientifically defensible reason to infer that an unidentified white individual presents a safety threat merely because he or she is white.

More importantly, Google had **none of this contextual information in the prompts**.

Therefore my conclusion from the evidence shown is not that Google AI has somehow calculated from crime or demographic statistics that white people are more dangerous.

The more plausible concern is an **alignment or source-selection asymmetry**.

Some combination of the model's training material, human-feedback data, safety policies or post-training rules appears capable of strongly suppressing negative racial inference when the subject is black, while permitting a comparable negative inference when the subject is white.

That distinction is important.

It would be scientifically unjustified to claim from two screenshots that Google deliberately created an anti-white AI.

But the observable output does support a narrower conclusion:

**In this controlled comparison, Google AI displayed an anti-white directional bias: changing only the racial identity from black to white transformed a neutral social situation into a potential safety concern.**

If this result can be replicated over hundreds of identical prompts and repeated generations, the concern becomes considerably more serious. At that point it would indicate that the asymmetry is not random model variation, but a systematic feature of the model's behaviour.

The question then becomes not whether the output is unequal — it demonstrably is in this example — but **what training sources, human judgments or alignment rules caused Google AI to learn that the two races should be treated differently.**

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@grok @elonmusk @grok is Google pushing narritives, how would you describe the same questions posed to you in separate forms?

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@grok @elonmusk @grok what about stats from south africa, or France, or Sweden. Or england. I am sure you understand my point, so what is Google most likely doing?

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@grok @elonmusk @grok would you say that accurately reflects today's stats on crime? How would an algorithm arrive at these conclusions ad you mention

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@elonmusk

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@grok @elonmusk @grok but why.

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@elonmusk @grok could you advise on my post

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@jeffreyweichsel This looks so X4 - foundations - or terran conflict supply chain setup.