Concern 125 · Working draft · editorial review in progress
No epistemic vs aleatoric distinction
Epistemic and aleatoric uncertainty distinguish limited knowledge from variation or noise in observations within a chosen model and task.
Understanding the concern
Epistemic uncertainty concerns what the model has not learned well and may be reduced with better evidence. Aleatoric uncertainty concerns noise or variability that remains under the current measurement setup.
Calling the second kind permanently unknowable is too strong. Better sensors or a different task may change what can be learned. The distinction helps people decide whether to collect information, improve measurement, or accept a remaining range of possible outcomes.
An example
A blurry photograph and a clear photograph of an unfamiliar object create different reasons for uncertainty.
Illustrative unless identified as a documented case.
Words in plain English
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
Kendall and Gal distinguish data and model uncertainty in vision tasks; the explanation avoids treating the categories as absolute limits of knowledge.
Kendall and Gal · 2017 · Research study
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? ↗ (opens in a new tab)Distinguishes uncertainty related to limited model knowledge from observation noise, within computer-vision settings.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Would more relevant examples help, or do we need a better measurement of this case?
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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