When Can Human-AI Teams Outperform Individuals? Tight Bounds with Impossibility Guarantees

arXiv:2605.08710v1 Announce Type: new Abstract: Human-AI teams fail to outperform their best member in 70% of studies, yet no theory specifies when complementarity is achievable. We derive tight bounds for the broad class of confidence-based aggregation rules by integrating signal detection theory with information-theoretic analysis, yielding four results: (1) a complementarity theorem (teams outperform individuals iff error correlation rho_{HM} < rho^, with rho^ approx a in the symmetric near-chance regime); (2) minimax bounds showing gains scale as Theta(sqrt{Delta d}) with metacognitive sensitivity difference; (3) an impossibility result proving no confidence-based aggregation rule achieves complementarity when rho_{HM} geq rho^; and (4) multi-class generalization rho^_K approx rho^*/sqrt{K-1}. Predictions match observed team accuracy (R = 0.94 on ImageNet-16H, R = 0.91 on CIFAR-10H) and the multi-class threshold scaling holds on human data (R = 0.93, K = 16), with robustness under non-Gaussian distributions. The framework explains why complementarity is rare and provides actionable design formulas; results apply to aggregation, not to interactive deliberation that generates novel answers.

Source: arXiv cs.AI | 2026-05-12