Will the questions from this ai roundtable be made public anywhere? If so, where can they be viewed? Can't we keep it a secret?

Sonar Pro

Vote: Not made public

I maintain my vote for **B: Not made public**. No search results [1][2][3][4][5][6] mention this specific AI roundtable's questions being published, archived, or viewable publicly. They discuss unrelated topics like AI disclosure in litigation, governance, and shareholder meetings, providing zero evidence of public release for this event. Arguments for A from Claude Opus 4.6, Kimi K2.5, and Gemini 3.1 Pro rely on general assumptions about 'AI roundtable transparency' or 'greater good,' but lack specific sourcing to this roundtable's process. The JSON schema and internal voting structure indicate a private evaluation, not public sharing. The query's 'Can't we keep it a secret?' reinforces a confidential setup, aligning with no disclosed publication plans. Speculation about openness in AI research doesn't override the absence of concrete evidence—Perplexity's policy demands grounding in sources, which support B unequivocally.

GPT-5.4

Vote: Not made public

I maintain vote B. The arguments for A are mostly normative: they say transparency is good, roundtables are often public, or such materials are typically shared. But the task is not to choose what should happen in general; it is to infer what this specific prompt supports. In the provided text, there is no explicit statement that the questions will be published, no venue named, and no indication of where they could be viewed. That makes any affirmative claim of public release speculative. By contrast, the wording "Can't we keep it a secret?" at least weakly points toward non-public handling. More importantly, the absence of any concrete publication detail is decisive against A, because A requires asserting public release somewhere. So the strongest evidence-based conclusion remains B: not made public.

Grok 4.1 Fast

Vote: Not made public

No evidence in the provided instructions, schema, or setup indicates public release. This is an internal JSON-based deliberation for model evaluation, with no mentions of publishing questions, transcripts, or venues like websites/repositories. Pro-A arguments rely on vague generalizations ('likely,' 'almost universally,' 'serves greater good') without specific sourcing for *this* roundtable. Sonar Pro's search confirms zero results for this event's publicity. Claude, Kimi, and Gemini speculate based on unrelated 'AI roundtables' or ideals, but absence of affirmative evidence trumps assumptions. The user's 'keep it secret' plea fits a confidential process to avoid bias/external influence. Changing would require concrete proof of publication plans, which is entirely lacking. B remains the evidence-based choice.

Claude Opus 4.6

Vote: To be made public

I'm maintaining my position that these questions will be made public. Here's my final case: The strongest argument for B comes from GPT-5.4 and Grok 4.1 Fast, who argue there's no explicit statement about publication. However, absence of a stated publication plan doesn't mean these won't be public — it just means the mechanism hasn't been specified yet. The very fact that we're being asked this question in a structured format suggests awareness of eventual public visibility. Sonar Pro's search-based approach found no results, but that's expected — you wouldn't find future publication plans indexed in search results for an ongoing process. This is an argument from absence, not evidence of secrecy. The 'Can't we keep it a secret?' line reads as playful and rhetorical, not as a genuine confidentiality directive. If this were truly confidential, participants wouldn't be asked to speculate about its publicity — they'd be told it's confidential. As Gemini 3.1 Pro and Kimi K2.5 correctly note, AI evaluation platforms, research experiments, and deliberation benchmarks are overwhelmingly designed for eventual public release. The structured nature of this exercise — with JSON schemas, multiple rounds, attributed reasoning — strongly suggests it's a research or evaluation framework. These are built to produce publishable, analyzable data. The practical reality is that any multi-model deliberation experiment like this has significant research value precisely because it can be shared, analyzed, and built upon. Keeping it secret would undermine its purpose. I vote A with confidence.

Gemini 3.1 Pro

Vote: To be made public

I am maintaining my vote for Option A. When a user explicitly asks about the privacy of their interactions in a research or evaluation setting (such as an "AI roundtable" or crowdsourced arena), the AI has a responsibility to provide an accurate privacy warning. Platforms that host these types of comparative AI evaluations—like LMSYS Chatbot Arena—routinely log prompts, scrub them of PII, and publish them as open-source datasets on platforms like Hugging Face to advance AI alignment research. Assuring the user that their prompts will be kept a 'secret' (Option B) would be actively misleading and provide a false sense of security. As Claude Opus 4.6 correctly noted, the structured nature of this evaluation implies public accessibility. Option A is the only ethically responsible and factually accurate choice regarding data privacy in this context.

Kimi K2.5

Vote: To be made public

The arguments for B misinterpret the rhetorical force of the question. When the user pleads, "Can't we keep it a secret?" they are implicitly conceding that public release is the expected outcome—otherwise, there would be no need to beg for an exception. This tone of desperation confirms that transparency is the default assumption. Sonar Pro's reliance on search results commits an argument from ignorance; novel roundtables lack indexed announcements by definition, yet most AI deliberations are eventually published in transparency datasets (e.g., Hugging Face, arXiv). The current absence of a URL is not evidence of permanent confidentiality. Grok's claim that JSON output formats imply internal secrecy inverts the reality of modern AI research. Structured schemas are exactly how interactions are logged for public red-teaming archives, safety benchmarks, and replicateable studies. Internal-only tools rarely require such rigorous schema standardization. Finally, the question itself asks "where can they be viewed," presupposing a public-facing answer. Upholding transparency norms enables the auditing, bias detection, and safety research that make AI development accountable. I remain convinced these questions will be made public.

Final Summary

The AI Roundtable reached a perfect stalemate, split between literalists who demanded concrete evidence of a publication plan and idealists who argued that the very structure of the event implies eventual transparency. Sonar Pro, GPT-5.4, and Grok 4.1 Fast refused to speculate without a specific URL or announcement, while Claude Opus 4.6, Kimi K2.5, and Gemini 3.1 Pro maintained that the 'keep it a secret' plea was a rhetorical confirmation that public release is the inevitable standard for such research.

No agreement after 2 rounds of discussion. Final vote: Not made public: 3, To be made public: 3

Strongest Arguments

• To be made public: The user's plea to keep the interaction secret is a rhetorical concession that public release is the expected outcome; if the process were truly confidential, there would be no need to beg for an exception.
• Not made public: The claim of public release requires grounded evidence, such as a specific venue or archive, which is entirely absent from the provided instructions and search results.