OmniMechanism Design for Human-AI Collaboration with Impressionable Minds

arXiv cs.AINovember 3, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2611.00441v1 Announce Type: new Abstract: As AI systems are increasingly deployed in high-stakes domains such as healthcare, effectively integrating human expertise with AI outputs is critical for reliable decision-making. A substantial body of theoretical work studies optimal mechanisms for human-AI collaboration (HAC), focusing on when to involve humans and how to combine their expertise with AI predictions. However, these approaches largely overlook a key factor highlighted by empirical evidence: human judgments are impressionable and can vary depending on how AI outputs are presented. For example, when AI systems defer decisions to humans, revealing or withholding the AI prediction

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