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Evidence Independence

Principle that agreement from shared training bias is not real consensus

Evidence independence is the principle that apparent agreement among AI models does not constitute genuine consensus if the agreement stems from shared training data rather than independent evidence. Models trained on overlapping corpora can converge on the same errors, making "consensus" a reflection of bias rather than truth.

This is a known limitation of the Multi-AI Consensus Protocol. The protocol treats model disagreement as a red flag, but agreement provides only weak assurance because the models are not independent observers.

True verification requires tracing claims to independent sources and evidence spans outside the training distribution. The Operator must remain skeptical of AI agreement and seek external confirmation for high-stakes claims.

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  • -Across the Sprachraums
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