Artificial intelligence abstention gives a system an outcome other than forced prediction. The model or surrounding decision layer can gather more information, return an uncertainty statement, route the case to another method, or defer to a person when the estimated risk exceeds an approved threshold.
A useful abstention policy balances the harm of wrong actions against the cost of unanswered cases. It must be evaluated for both error reduction and coverage, because a system that abstains on every difficult case may look accurate while failing the people who most need support.
Acronyms and aliases
AI abstention acronymselective prediction synonymartificial intelligence abstention variantmodel abstention variant
Related terms
Frequently asked questions
When should an artificial intelligence system abstain?
It should abstain when uncertainty or risk exceeds the approved threshold, required evidence is missing, or the action lies outside its permissions.
How is artificial intelligence abstention evaluated?
Teams measure accuracy among answered cases, the proportion of cases covered, the cost of deferrals, and whether abstention is fair across relevant groups.
