AI systems face uncertainty when information is incomplete, ambiguous, conflicting, or unavailable during decision-making.

Uncertainty is normal in AI because models rely on probabilities rather than guaranteed answers.

Common causes include missing context, conflicting data, hallucinations, and unfamiliar situations.

Confidence scoring helps AI decide whether to act, ask questions, or escalate decisions.

Human oversight and fallback mechanisms improve reliability during uncertain situations and critical tasks.

Multiple data sources and better context management help AI make more accurate decisions.

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