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Model disagreement helps spot risky inputs by measuring how much predictions vary. Using metrics like ensemble variance or margin, you can flag cases where models don’t agree
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AI models often hallucinate silently, risking costly errors in critical tasks. This article explores how leveraging model disagreement can serve as a practical check, improving auditability and supporting thorough due diligence
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Model disagreement helps spot inputs where predictions vary a lot, signaling potential risk. Tracking metrics like ensemble variance or prediction margin highlights these tricky cases