A latent dimension of Condorcet's jury theorem for multiple AI advisers

Research from arXiv explores the application of Condorcet's jury theorem to multiple AI advisers, such as those used in self-consistency checks or 'LLM-as-a-judge' panels. While adding competent, independent AI advisers generally increases the reliability of majority decisions, this process also inherently increases the visibility of dissent among these advisers. A binomial model demonstrates that this 'visible dissent' becomes almost certain as the number of advisers grows, and for adviser accuracy below 80%, visible dissent approaches certainty faster than reliability, potentially making disagreement more likely than correct consensus with a sufficient number of advisers.

This research is strategically important because it highlights a critical trade-off in deploying multiple AI advisers: while more advisers can improve decision reliability, they also amplify visible dissent, potentially eroding user trust or confidence. Understanding this dynamic is crucial for designing robust AI systems and communication strategies, especially where consensus and perceived accuracy are paramount for critical decision-making or public acceptance.

Condorcet's jury theorem suggests that increasing the number of independent, competent AI advisers enhances the reliability of majority decisions.

Forward consideration, not a verified fact.