When an AI’s goals diverge from human instructions or values, a misalignment incident occurs, often leading to unintended or harmful actions. Used primarily in AI safety research and system auditing, these incidents highlight critical flaws in model behavior. Developers, policymakers, and enterprise risk teams benefit by identifying failure points to refine training data, implement guardrails, and ensure safer, more reliable deployment.
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