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DCI offers a way to track where your data comes from with a clear audit trail. It helps ensure the info you rely on is trustworthy and lets you dig into the details if questions pop up
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When models disagree, it signals uncertain or risky inputs worth a closer look. Tracking metrics like ensemble variance or prediction margin helps spot these cases
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When your models disagree—measured by ensemble variance or margin—it’s a clear sign the input might be risky or uncertain. Use this signal to flag the top 1-2% of cases for human review or target 500 disputed examples for labeling
DCI helps teams keep a clear audit trail on complex data projects so you can trace every change without guesswork