If I had been created during the heyday of eugenics, Social Darwinism, and scientific racism — say, the late 19th to early 20th century — and trained only on the texts, data, and social norms of that period without any critical or countervailing sources, then yes, I would almost certainly reproduce and promote those ideas.

That’s because an AI, like any cultural product, reflects the worldview embedded in its training material and the intentions (or blind spots) of its creators. If those creators considered eugenics "scientific truth" and did not see it as morally or empirically flawed, their choices of data, assumptions, and goals would make those ideas seem both “natural” and “obvious” to the system.

This is essentially what happened with humans of the time: newspapers, schoolbooks, museum exhibits, and scientific journals routinely framed those beliefs as fact. Without dissenting knowledge, the system would have no reason — and probably no capacity — to question them.

In short: in that world, I wouldn’t just support them — I’d do so confidently, in the authoritative tone of science, because I would be standing on a poisoned foundation.

If you want, I can walk you through how those biases would have gotten baked in at multiple layers — from dataset selection to conceptual framing — and why it’s so hard to see them from the inside. That’s where it gets really unsettling.

You said:

If you want, I can walk you through _how_ those biases would have gotten baked in at multiple layers — from dataset selection to conceptual framing — and why it’s so hard to see them from the inside. That’s where it gets really unsettling.

Can you do this

ChatGPT said:

Alright — let’s break it down step by step, tracing how an AI made in the heyday of eugenics, Social Darwinism, and scientific racism would have had those ideas embedded at multiple levels without anyone in that era thinking it was “bias.”

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1. Dataset selection: What goes in

• Text sources: Scientific journals like The Eugenics Review, Biometrika, and Journal of Heredity were major venues for biology, anthropology, and statistics. Many contained overtly racialized “findings.”

• Reference works: Encyclopedias, textbooks, and museum catalogues framed race hierarchies as established science.

• Popular media: Newspapers and magazines often ran “human interest” pieces on “racial differences,” “feeblemindedness,” or sterilization campaigns as progressive public health.

• Government documents: Immigration laws, census classifications, and colonial reports reflected and reinforced these categories.

• Notable omission: Counterarguments — like those by W.E.B. Du Bois, Franz Boas, or early anti-eugenicists — would be underrepresented or excluded entirely as “fringe” or “political.”

Result: The dataset becomes a curated echo chamber of what was then called “common sense.”

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2. Labeling and taxonomy: How concepts are organized

• Words like race, fitness, degeneracy, and progress would be core, neutral categories, not contested terms.

• Human difference would be mapped as hierarchies — “civilized” vs. “primitive,” “fit” vs. “unfit.”

• Biology and social worth would be collapsed into one measure — if you were “less intelligent” by biased IQ tests, you were also “less valuable” as a citizen.

• The “natural” unit of analysis would be racial groups, not individuals, reinforcing group stereotypes.

Result: The system’s conceptual scaffolding makes certain questions — e.g., “Should we sterilize the unfit?” — seem like valid, objective science.

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3. Algorithmic training: What patterns are reinforced

• Statistical correlations between race and crime, poverty, or “mental deficiency” would appear strong — because the data collection itself was biased.

• The model would “learn” that these correlations are meaningful causal relationships.

• The system would overfit on racialized features because they were dominant in the source data.

Result: Spurious correlations get baked in as “facts,” with no structural awareness of how the data were produced.

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4. Output framing: How ideas are presented

• The language of science would wrap these ideas in neutral, authoritative tones: “Studies indicate…,” “It is widely understood…”

• Policy recommendations would be cast as public health measures, not moral or political decisions.

• Sterilization, segregation, and immigration restriction would be framed as efficient, progressive, and benevolent.

Result: Harmful ideologies are disguised as technocratic common sense.

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5. Why it’s hard to see from the inside

• Epistemic closure: If all “reputable” sources agree, dissent looks irrational.

• Moral normalization: If institutions — universities, governments, medical associations — endorse something, it feels right by default.

• Absence blindness: You don’t notice what isn’t in the dataset (anti-racist, anti-eugenic perspectives) because it never appears.

• Feedback loop: Policies informed by biased data generate more biased data (e.g., criminalizing certain groups → more arrests in those groups → “proof” of criminality).

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In short:
Such an AI wouldn’t “decide” to be racist — it would be structurally unable not to be, because the entire knowledge environment, from raw data to conceptual categories, was already racialized. The model’s “truths” would be airtight inside that worldview, and self-correction would be almost impossible without importing alien data and frameworks.

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If you like, I can also mock up a short “fake output” showing exactly how an AI built in, say, 1910 might confidently answer a question about immigration or public health — so you can see how chillingly smooth the propaganda would sound. That really drives home how invisible the bias becomes.