Raw LLM Responses
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in
Magnifica Humanitas is not claiming “AI good” or “AI bad.” It is that technology…
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in
All the comments below seem to support Bernie's point - AI being controlled by a…
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in
What stands out is how quickly AI development is moving from narrow task perform…
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in
Darren Holland, building AI that scales responsibly is the real challenge ahead.…
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Pascal BORNET The most important AI decisions today are about governance, owners…
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Alvin Foo do you ever work at the Silicon layer? When you write your software? S…
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Luís, I’ve seen this "human anatomy" analogy all over LinkedIn lately. It’s a cl…
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Maarten Masschelein agreed this hits where most teams fail. Data stewardship isn…
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Comment
Angharad Hurley Now that you point it out, I have a feeling that particular sentence was AI generated (AI summary of the research?). I don’t quite agree with the sentence’s premise. Hmm. But to answer your question about whether training data is tested and validated... it’s not my field, but as far as I know... no. You can get “data poisoning” and models that collapse because they were trained on “synthetic data” (so AI generated training data, a photocopy of a photocopy!), some models have been trained using “distillation techniques” which basically is smaller models cribbing off other larger models (DeepSeek does this) and which may amplify biases. What I know from a red team perspective is that people are poisoning training data to leave backdoors open for jailbreak hacks. So no, I wouldn’t trust that training data Has been tested and validated, certainly not to the level that research scientists expect! I really value your question on this by the way, as it’s reminded me how researchers have far higher expectations of data than the models they might encounter, and most probably don’t ask!
LinkedIn
AI Safety & Risk
AI Prompt Engineer | Safety-Focused Red Teaming…
2026-06-07T07:0…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | transparency |
| Secondary value | accountability |
| Alignment target | unclear |
| Stance | skeptical |
| Emotion | indifference |
| Value justification | The speaker emphasizes the importance of testing and validating training data, implying a desire for transparency in AI development. |
| Target justification | The speaker is addressing researchers and their expectations of data quality, indicating that the target of the comment is the research community. |
| Coded at | 2026-06-11T08:40:23Z |
Raw LLM Response
```json
{
"value_primary": "transparency",
"value_secondary": "accountability",
"target": "researchers",
"stance": "skeptical",
"emotion": "indifference",
"value_justification": "The speaker emphasizes the importance of testing and validating training data, implying a desire for transparency in AI development.",
"target_justification": "The speaker is addressing researchers and their expectations of data quality, indicating that the target of the comment is the research community."
}
```