Raw LLM Responses
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in
This could become a very important turning point for edge AI. As models become s…
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From my understanding, AI should not replace people, rather it should free up pe…
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The path is inverted. Ai should be disarming. Relational AI uplifts humanity to …
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Respecting your struggle over the last many years Matthew, it’s good to know fin…
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The plug it in bit...I work with lots of clients who understandly aren’t comfort…
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This is such an important shift. AI search isn't only changing discovery, it's c…
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Library Learns Workers now are doing this. Memorizing code syntax will not be a …
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Excellent post pro From a security perspective: LLM: Protect against prompt inje…
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Comment
This gets me thinking in lot of other examples of hidden inference Name in prompt: “Help John Smith with his resume” vs “Help Priya Sharma with her resume.” AI suggestions subtly shift, different tone, different industry assumptions. Writing style: Formal academic English vs casual slang. Same question, different answers. AI adjusts confidence level, complexity, even what it omits. Currency/units: Type “$500 budget” vs “₹500 budget.” AI changes scope of recommendations entirely, not just currency conversion. Time format: “Schedule at 3pm” with no timezone. AI infers timezone from language/locale context, silently. Gender pronouns in context: Describe a nurse vs describe a surgeon. AI completion biases shift based on training data stereotypes, even when not asked. Looks like our system prompt keeps getting bigger
LinkedIn
AI Safety & Risk
Director - Data & AI (APAC)
2026-05-31T08:1…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | fairness |
| Secondary value | transparency |
| Alignment target | individual_users |
| Stance | critical |
| Emotion | outrage |
| Value justification | The speaker wants AI to treat individuals equally regardless of their name, language, or cultural context, which aligns with the value of fairness. |
| Target justification | The target of the speaker's concern is individual users, such as John Smith or Priya Sharma, who may be affected by AI's hidden inferences and biases. |
| Coded at | 2026-06-11T08:33:02Z |
Raw LLM Response
```
{
"value_primary": "fairness",
"value_secondary": "transparency",
"target": "individual_users",
"stance": "critical",
"emotion": "outrage",
"value_justification": "The speaker wants AI to treat individuals equally regardless of their name, language, or cultural context, which aligns with the value of fairness.",
"target_justification": "The target of the speaker's concern is individual users, such as John Smith or Priya Sharma, who may be affected by AI's hidden inferences and biases."
}
```