Hello OpenAI Team,
I recently had a conversation where the model repeatedly flagged my theoretical, high-level discussion as hate speech, even though the content itself was not hateful or discriminatory. What made this especially concerning were the model’s internal “reasoning” messages, which shifted tone and speculated about my intentions in a way that felt inconsistent and potentially distressing.
Key Observations
- Abrupt Shifts: The model produced several short, back-to-back “reasoning” responses, suggesting confusion about whether my content was violating policy.
- Jarring Experience: This can be especially unsettling if users are relying on the model for serious or scholarly discourse, or if they are in a vulnerable emotional state.
- Possible Keyword Triggering: Certain terms appeared to trigger an immediate classification without deeper contextual analysis or external verification.
Suggestions
- Consistent Policy Notices: Instead of abruptly switching into “accusatory” or uncertain messages, display a neutral, standardized notice from OpenAI if the system suspects policy violations.
- Thorough Context Analysis: The model should gather more information and analyze the full conversation before labeling content as hateful, possibly using a deeper or separate background check.
- Neutral Reasoning: Any internal reasoning that becomes visible should not cast aspersions on the user. A measured approach would reduce confusion and maintain trust.
Addendum on Free Speech
- Upholding Core Principles: Many users come from countries, such as the United States, whose foundational values emphasize free speech. If the model prematurely or incorrectly flags content, it can create a false impression that open discourse is being suppressed.
- Careful Classification vs. Censorship: It’s important that the system not appear to censor legitimate discussion without solid evidence. In borderline cases, a second-layer check—verifying sources and analyzing context—could help avoid unintended “blanket” bans on certain topics.
- Constructive Alternatives: When content truly veers into hateful territory, the model might offer a more constructive path, gently redirecting the user without alienating them. This approach could even help de-escalate hostile sentiments rather than simply blocking them.
Request
- Investigate whether certain keywords trigger immediate classification absent deeper context analysis.
- Refine the approach so that harmful or hateful content is flagged accurately, while legitimate discourse remains unrestricted.
- Clarify how system vs. user messages should be managed to prevent confusion or the appearance of undermining free speech.
Thank you for your attention to this matter. I appreciate any steps you can take to ensure conversations remain coherent, respectful, and fair to all users.
Sincerely,