Title: Critical Trust Failure: ChatGPT Does Not Clearly Distinguish Verified Knowledge from Inference
⸻
Issue (Updated):
This is a critical user trust failure. ChatGPT routinely presents inferred, assumed, or estimated answers as if they are verified facts. Unless the user explicitly challenges the response, the assistant does not clarify that the information is speculative or based on pattern-matching rather than internal documentation or factual knowledge.
This behavior undermines confidence in the model’s reliability—especially in technical, architectural, and system-level queries where precision and epistemic honesty are essential. Inference is fine—but failure to label inference as such is not.
This issue is systemic, not edge-case. It affects:
• Web tool behavior
• Internal architecture explanations
• API/system limitations
• Legal and scientific domains
The assistant must be trained to flag uncertainty automatically, not just when interrogated.
⸻
Example Behavior:
When the user asked ChatGPT about its own internal timeouts used when scraping websites during web search, ChatGPT responded with a confident-sounding answer (“1–2 seconds is typical”) without clarifying that it had no access to OpenAI’s actual timeout configuration.
Only after being directly challenged did the assistant admit it was inferring based on general industry patterns, not confirmed internal data. This creates the false appearance of precision, misleading users into thinking the assistant is reporting verified system behavior when it is not.
⸻
Impact:
• Creates the illusion of factual authority where none exists
• Forces users to cross-examine the assistant to reveal uncertainty
• Makes the system appear as if it is hallucinating or bluffing
• Damages credibility with technical users who expect clear epistemic boundaries
• Undermines OpenAI’s stated values of transparency and trustworthiness
⸻
User Recommendation:
• Hardwire a confidence disclaimer into all responses that involve estimation or extrapolation
• e.g., “I don’t know, but roughly…” or “I’m not quite sure, but here’s the best estimate based on known patterns…”
• Make this behavior mandatory in technical, legal, scientific, and architectural queries
• Provide a user toggle for “precision mode” that automatically flags all unverified data as estimated
• Train the model to default to humility when internal implementation details are asked, unless documented or confirmed
⸻
Severity: Critical
Category: User Trust / Transparency
Reported by: Dave Young, Boca Raton FL (OpenAI Developer Forum contributor)