What would actually make you trust an AI moderator's findings?

We launched Mira on Product Hunt today, and the conversations in the comments have been the most interesting part of the whole day.

Researchers asked about probe neutrality — whether the follow-up questions an AI asks mid-interview can lead a participant, rather than uncover them. They asked about cultural calibration, whether emotion models trained largely on Western data can accurately read a participant in Jakarta or Nairobi. They asked whether the say/feel mismatch gets surfaced as raw evidence or quietly resolved into a single confidence score.

These are serious questions. And they made me realize something: the bar for trust in AI-moderated research is fundamentally different from other AI tools.

If a writing assistant gets something wrong, you catch it before you publish. If an AI coding tool hallucinates, your tests fail. But if a research tool misreads how participants felt during a concept test, and that feeds into a product decision, the error is invisible. By the time the product ships and the market responds, the research moment is long gone.

So the question of trust isn't abstract. It has real downstream consequences.

I want to ask this directly to researchers, practitioners, and anyone who works with qualitative data: what would it actually take for you to trust the findings from an AI-moderated interview the same way you'd trust a carefully run human-moderated session?

Is it full transparency into how emotional signals are weighted?
The ability to see raw signals and override them before they appear in a report?
Proof of regional validation against local human coders?
Something structural about how probes are designed?
Or is there a ceiling, something you believe only a human moderator can do?

Not asking to defend Mira. Asking because I think this is a question the whole space needs to answer honestly, and I'd rather hear it from people doing the work than assume we already know the answer.