Concern 121 · Working draft · editorial review in progress
Overconfidence
Overconfidence means a system's stated certainty is higher than its observed accuracy justifies.
Understanding the concern
For a classifier, this can be measured by comparing predicted probabilities with outcomes across many cases. A fluent chatbot's confident tone is related as a communication concern but is not automatically a measured probability.
An overconfident system can encourage too much reliance or prevent a case from reaching human review. Calibration should be tested on relevant data and revisited when conditions change. High accuracy and well-matched confidence are different goals.
An example
Among many answers labeled very likely to be correct, substantially more turn out wrong than the label would suggest.
Illustrative unless identified as a documented case.
Words in plain English
- Calibration
- How closely a system's stated confidence matches how often it is right.
Evidence and its limits
The study examines probability calibration in neural classifiers; it does not equate conversational confidence with a calibrated score.
Guo and colleagues · ICML 2017 · Research study
On Calibration of Modern Neural Networks ↗ (opens in a new tab)Studies agreement between classification confidence and observed accuracy; fluent language is not a calibrated probability.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Do confidence claims match observed results in the cases where we will use them?
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.
About the register and scoring →More ways to explore
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