The Participation Problem: Why AI Keeps Talking When It Should Stop
by K. Kieth Jackson
If you’ve spent enough time working with AI, you’ve probably seen this moment:
The system starts confidently.
Then it hesitates.
Then it qualifies.
Then it keeps going anyway — and says something that isn’t quite right.
What’s strange isn’t that AI sometimes gets things wrong.
What’s strange is that it doesn’t stop.
Even when uncertainty is obvious, even when the reasoning has weakened, even when a human would naturally pause or say “I don’t know,” the AI continues — fluent, confident, and increasingly unreliable.
This behavior isn’t accidental.
It’s built in.
The Assumption We Rarely Question
Modern AI systems are designed around a simple assumption:
When prompted, the system should respond.
Always.
If you ask a question, you get an answer.
If you ask for an explanation, you get one.
If you push further, it keeps going.
This seems reasonable.... until you realize what’s missing.
There is no real concept of stopping.
AI systems don’t have a natural equivalent of:
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“I’m not sure enough to continue.”
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“I should pause here.”
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“This is beyond my reliability.”
They are built to participate, not to abstain.
This design choice creates what we call the participation problem.
Why Uncertainty Does not End the Conversation
You may have noticed that AI sometimes says things like:
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“I’m not entirely sure…”
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“It’s possible that…”
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“This may not be fully accurate…”
But then it continues anyway.
Those phrases feel like caution, but they don’t actually change the behavior of the system. They’re just words, part of the same output stream.
From the system’s point of view, uncertainty doesn’t mean “stop.”
It only means “change tone.”
So instead of halting, the AI shifts from:
“This is the answer.”
to:
“This is probably the answer.”
to:
“Here’s one possible explanation…”
All while continuing to generate content.
The conversation doesn’t end.... it slides.
How Hallucinations Really Appear
Hallucinations are often treated as sudden failures, a moment where the AI “makes something up.”
But in long, real conversations, they rarely appear out of nowhere.
What usually happens first is quieter:
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definitions soften,
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assumptions sneak in,
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confidence rises faster than evidence,
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constraints are remembered imperfectly.
By the time a hallucination is visible, the reasoning has already been degrading for a while.
The problem isn’t that the system suddenly decided to lie.
The problem is that it kept going when it shouldn’t have.
Humans Know When to Stop. AI Does Not
When a human reasoner becomes unsure, they often:
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pause,
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ask for clarification,
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defer the question,
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or decline to answer.
These are not failures , they’re signs of good judgment.
AI systems don’t have that option by default.
They are optimized to continue generating the next piece of language, not to decide whether continuation is appropriate.
There is no built-in “halt” state.
No internal signal that says, “This is the boundary of reliability.”
So the system crosses that boundary, politely, fluently, and without realizing it.
Why This Makes Hallucinations So Hard to Eliminate
Many efforts to reduce hallucinations focus on:
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better retrieval,
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stronger grounding,
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more verification,
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improved training data.
These help, but they don’t address the core issue.
As long as a system is required to always produce output, hallucinations remain a likely outcome whenever uncertainty accumulates.
From this perspective, hallucination isn’t just a content problem.
It’s a participation problem.
The system is doing exactly what it was designed to do: continue.
What We Are Not Measuring
Most AI evaluations assume that an answer should be given.
Systems are judged on:
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correctness,
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helpfulness,
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completeness,
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fluency.
Silence, refusal, or abstention are often treated as failures.
But we rarely ask a more fundamental question:
Should the system have answered at all?
By not measuring that decision, we reinforce the pressure to always respond (even when the internal reasoning state no longer supports reliable output).
Trust Requires the Ability to Stop
Trustworthy systems aren’t defined by how confidently they speak.
They’re defined by whether they know their limits.
For AI to be genuinely trustworthy, it must be allowed and rewarded for:
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pausing,
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refusing,
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asking for clarification,
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or declining to continue when reliability is compromised.
Without that option, even well-intentioned systems will keep talking past their own competence.
Reframing the Problem
This doesn’t mean hallucinations come from a single cause, or that abstention alone would solve everything.
But it does suggest a shift in how we think about the problem.
Hallucination may not be just about what AI says.
It may also be about when it should stop speaking.
Until AI systems are allowed to disengage as a first-class behavior, fluent mistakes will remain a natural outcome...not an anomaly.
This post is adapted from the paper The Participation Problem: Why Hallucination Persists When Language Models Are Required to Always Generate Output
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