When do human–AI combinations achieve complementarity?

Determine the specific conditions and collaboration designs under which human–AI combination strategies achieve complementarity, defined as producing higher decision accuracy than both human-only and AI-only baselines, across different tasks and domains.

Background

The paper contrasts the dominant AI-as-advisor workflow with the hybrid confirmation tree (HCT), emphasizing how independence between human and AI judgments can improve accuracy. Despite these results, the broader literature lacks a clear understanding of when and which human–AI collaboration structures outperform both humans and AI individually.

Identifying the conditions for complementarity involves specifying task characteristics, relative accuracies of humans and AI, interaction structures (e.g., independent vs. advisory), and decision aggregation rules that yield combined performance gains beyond either component alone.