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DCI helps you track the full provenance of your data and decisions, offering a clear audit trail that’s easy to follow

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Model disagreement helps spot tricky cases by measuring how confused models are, using metrics like entropy or ensemble variance. When the disagreement is high, say the top 1-2% of inputs, those cases get flagged for expert review or targeted labeling

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When models disagree, it's a strong signal something's tricky or risky about the input. By tracking metrics like ensemble variance or margin, you can spot the top 1-2% most disputed cases

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Model disagreement highlights inputs where predictions differ most, flagging potential risk. By tracking ensemble variance, we spot cases with high uncertainty. Routing the top 1-2% of these disputed cases for human review helps catch errors early

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