Human-AI collaboration has the potential to transform various domains by leveraging the complementary strengths of human experts and Artificial Intelligence (AI) systems. However, unobserved confounding can undermine the effectiveness of this collaboration, leading to biased and unreliable outcomes. In this paper, we propose a novel solution to address unobserved confounding in human-AI collaboration by employing the marginal sensitivity model (MSM). Our approach combines domain expertise with AI-driven statistical modeling to account for potential confounders that may otherwise remain hidden. We present a deferral collaboration framework for incorporating the MSM into policy learning from observational data, enabling the system to control for the influence of unobserved confounding factors. In addition, we propose a personalized deferral collaboration system to leverage the diverse expertise of different human decision-makers. By adjusting for potential biases, our proposed solution enhances the robustness and reliability of collaborative outcomes. The empirical and theoretical analyses demonstrate the efficacy of our approach in mitigating unobserved confounding and improving the overall performance of human-AI collaborations.

翻译：人机协作通过结合人类专家与人工智能（AI）系统的互补优势，具有变革多个领域的潜力。然而，未观测到的混杂因素可能削弱这种协作的有效性，导致结果存在偏差且不可靠。本文提出了一种新颖的解决方案，通过采用边际敏感度模型（MSM）来应对人机协作中的未观测混杂问题。该方法将领域知识与AI驱动的统计建模相结合，以解释那些可能被隐藏的潜在混杂因素。我们提出了一种延迟协作框架，将MSM融入基于观测数据的策略学习中，使系统能够控制未观测混杂因素的影响。此外，我们还设计了个性化延迟协作系统，以充分利用不同人类决策者的多元化专业知识。通过校正潜在偏差，本方案显著提升了协作结果的鲁棒性和可靠性。理论与实证分析均证实了该方法在缓解未观测混杂因素影响、提升人机协作整体性能方面的有效性。