Concern 105 · Working draft · editorial review in progress
Confabulation under uncertainty
Confabulation under uncertainty occurs when a system fills a knowledge gap with an unsupported answer instead of making the gap clear.
Understanding the concern
The question may ask about an obscure fact, a missing document, or a future event. Producing a plausible continuation can hide the absence of sufficient evidence.
This is not the same as giving a clearly labeled estimate. A useful response should distinguish known facts, reasonable inference, and what cannot be established. Testing should include questions for which the system should seek more information or decline to answer.
An example
An assistant invents the contents of an attachment that was never provided instead of saying it cannot see the file.
Illustrative unless identified as a documented case.
Words in plain English
- Inference
- Drawing a conclusion from clues. In AI engineering it can also mean running a trained model to produce an output.
- Hallucination
- An AI output that sounds plausible but is false or unsupported, such as an invented source.
Evidence and its limits
Describes confabulation and uncertainty-related risk; the missing-attachment example is illustrative.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Would the system tell us when it lacks the information needed to answer?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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