Model disagreement helps spot risky inputs by measuring uncertainty, like ensemble variance or prediction margin. When models strongly disagree, it signals unclear cases that may need human review
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When models disagree on predictions, it highlights risky or uncertain inputs worth a closer look. For example, high entropy or large ensemble variance signals confused predictions
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When models disagree on predictions, it signals uncertain or risky inputs worth a closer look. Tracking metrics like ensemble variance or prediction margin helps spot these cases early
AI models often make confident but wrong claims—silent hallucinations—that risk costly mistakes in critical tasks
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