Artificial intelligence uncertainty quantification aims to describe what a model does not know rather than returning only one answer. Methods may produce probability distributions, prediction intervals, ensembles, calibrated confidence values, or separate estimates for uncertainty caused by noisy data and limited model knowledge.
Uncertainty estimates can guide a system to gather more evidence, choose a safer action, or defer to a person. Their usefulness depends on calibration and coverage outside the training distribution, so estimates must be tested on representative errors, rare cases, and changing real-world conditions.
Acronyms and aliases
AI uncertainty quantification acronymUQ acronymartificial intelligence uncertainty quantification variant
Specialised terms
Related terms
Frequently asked questions
Why does artificial intelligence uncertainty quantification matter?
It helps systems avoid false certainty and supports safer decisions about when to act, collect more information, or request human review.
How can artificial intelligence uncertainty be represented?
It can be represented with probability distributions, intervals, ensembles, confidence estimates, or other calibrated measures tied to possible outcomes.
