Raw LLM Responses
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in
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7466317691755…
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7466223599805…
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This gets me thinking in lot of other examples of hidden inference Name in promp…
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Abhishek Veeramalla The gap between "I understand AI" and "I can build with AI" …
7466171547850…
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The key issue may not be only how capable AI systems become, but how clearly the…
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in
Atheism only says God is absent from the material world. It does not mean humans…
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SAURABH SINGH Interesting perspective Crazy how fast companies adopted AI withou…
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Most organizations are still experimenting with AI assistants. The UAE appears t…
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Comment
In the context of AI, informal data stewards are the people catching the problems that models will eventually amplify. The person who documents dataset quirks before they become training data assumptions is doing governance work that no formal review process will surface in time. That behaviour has always mattered, but even more now.
LinkedIn
AI Ethics & Trust
Co-Founder & CTO | Turning AI, Data & Platform …
2026-04-30T03:4…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | accountability |
| Secondary value | none |
| Alignment target | individual_users |
| Stance | demanding |
| Emotion | approval |
| Value justification | The speaker emphasizes the importance of informal data stewards in catching problems that AI models may amplify, highlighting the need for accountability in AI development. |
| Target justification | The target of the speaker's concern is individual users, specifically informal data stewards, who are responsible for ensuring the quality and reliability of data used in AI models. |
| Coded at | 2026-06-11T07:54:51Z |
Raw LLM Response
```
{
"value_primary": "accountability",
"value_secondary": "none",
"target": "individual_users",
"stance": "demanding",
"emotion": "approval",
"value_justification": "The speaker emphasizes the importance of informal data stewards in catching problems that AI models may amplify, highlighting the need for accountability in AI development.",
"target_justification": "The target of the speaker's concern is individual users, specifically informal data stewards, who are responsible for ensuring the quality and reliability of data used in AI models."
}
```