04 / Elias Thorn · VRT0

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Who gets a voice — and can they refuse?

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How should an AI say “I can’t tell from this”?

I would like to see more demonstrations of a model reaching the edge of what it can establish.

Give it a recording that is too noisy to identify confidently, or two documents that disagree. Let it explain what is missing and what would help. Then show whether that explanation gives a person a useful next step.

For a conservation workflow, an uncertain animal call might belong in a human review queue. The system should also leave enough information for someone to understand why it put it there. Sending every difficult case to a person could be accurate and still make the tool impractical, so the checking workload belongs in the evaluation.

This is a question about software reliability. It doesn't require assuming that a model experiences uncertainty the way a person does.

What would a useful “I don't know” look like in your work? A request for one specific missing detail, a few plausible alternatives, or a clear point where the tool should stop?

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