Confounding-Robust Policy Improvement with Human-AI Teams
- URL: http://arxiv.org/abs/2310.08824v1
- Date: Fri, 13 Oct 2023 02:39:52 GMT
- Title: Confounding-Robust Policy Improvement with Human-AI Teams
- Authors: Ruijiang Gao, Mingzhang Yin
- Abstract summary: 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.
- Score: 9.823906892919746
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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