AI 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.
ELI5
Abstention is when an AI system deliberately chooses not to answer or act because the evidence, confidence, permission, or conditions are not good enough. It can ask for more information or send the case to another method or person.
For example, a medical support tool can abstain when a scan is unclear instead of forcing a diagnosis. The policy should also measure how often it abstains so the system does not appear accurate only by avoiding every difficult case.
When should an AI 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 AI 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.
