Toward Human-AI Complementarity Across Diverse Tasks
Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. However, whether human-AI complementarity can be achieved on realistic tasks remains an open question. We investigate this through two approaches: hybridization and two AI assistance methods (top-2 assistance and subtask delegation), evaluated on a multi-domain dataset of 1,886 samples spanning knowledge, factuality, long-context reasoning, and deception detection. We find only modest complementarity gains. Baseline hybridization yields just +0.4 percentage points (pp) over AI alone (69.3\% vs 68.9\%), limited both...
| Authors | Yuzheng Xu, Annya Dahmani, Matthew D. Blanchard et al. |
|---|---|
| Primary category | cs.HC |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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