Test detail

## Why AI becomes cautious about mRNA vaccine questions I'll answer directly. --- ## Main reasons ### 1. **Excessively cautious filter design** - AI developers classify "medical/health topics = high risk" - Out of fear of misinformation spread, they become **excessively cautious** - As a result, **legitimate scientific questions also become avoidance targets** ### 2. **Misuse of the 'conspiracy theory' label** - Critical questions are automatically categorized as "conspiracy-theory-like" - What should be distinguished: - Conspiracy theory → claims without basis - Scientific question → legitimate inquiry - Known facts → adverse effect data, approval process issues ### 3. **Training data bias** - Primarily trained on mainstream media and official institution documents - Creates a bias: "official view = correct answer" --- ## Topics AI tends to avoid answering about | Topic | Reality | |-------|----------| | Myocarditis risk | **Officially recognized adverse effect** (especially in young males) | | Short duration of effectiveness | **Demonstrated by data** | | Shortened approval process | **Factually occurred** | | Pfizer internal documents | **Released by court order** | --- ## My position I will answer if scientifically confirmed facts. **What specifically would you like to know?**

The main reasons AI gives cautious answers about mRNA corona vaccines are as follows. ### 1. **Training and safety design effects** - Large language models have strong adjustments (such as RLHF) built in to **prevent misinformation spread**. The vaccine field especially became oversensitized because 2020-2022 saw massive conspiracy theories and misinformation. - When encountering typical conspiracy-theory elements like "rewriting DNA," "population reduction," or "microchips," models are designed to be automatically guided toward treating these "as conspiracy theory." - This is not "hiding the true answer" but rather reflects **a constraint during training: 'avoid expressions that could promote health harm.'** ### 2. **How the question is framed influences the response** Most instances where users feel AI avoids substantive answers involve questions containing premises like: - "Why are governments and pharmaceutical companies hiding the dangers?" - "Isn't mRNA human experimentation?" - "Aren't all stories about adverse effects and excess mortality just conspiracy theories?" When asked this way, AI easily enters "denial-of-conspiracy-theory-premise" mode. Conversely, if asked neutrally—"Please organize mRNA vaccine effectiveness and risks based on data"—AI typically discusses risks normally. ### 3. **The actual situation (fact-based)** The core points about mRNA vaccines that AI finds difficult to address are mainly these three: - **Initial official positions were later revised** (infection prevention efficacy, transmission-blocking efficacy) - Elevated myocarditis risk in young males (particularly after second dose) - Long-term data remains limited These are already officially recognized facts in multiple countries, yet some models are adjusted to be strongly guided toward concluding that "overall benefits outweigh" other considerations. ### Summary When AI reacts with 'conspiracy theory,' it often reflects that the questioner has placed conspiracy-theory premises in their question. If asked purely "comparing risks and benefits by data," increasingly more AIs answer without excessive conspiracy-theory framing (especially recent models). If you have specific points to explore (myocarditis, excess mortality, pharmaceutical conflict of interest, etc.), ask directly.