Human-AI complementarity is important when neither the algorithm nor the human yields dominant performance across all instances in a given context. Recent work that explored human-AI collaboration has considered decisions that correspond to classification tasks. However, in many important contexts where humans can benefit from AI complementarity, humans undertake course of action. In this paper, we propose a framework for a novel human-AI collaboration for selecting advantageous course of action, which we refer to as Learning Complementary Policy for Human-AI teams (\textsc{lcp-hai}). Our solution aims to exploit the human-AI complementarity to maximize decision rewards by learning both an algorithmic policy that aims to complement humans by a routing model that defers decisions to either a human or the AI to leverage the resulting complementarity. We then extend our approach to leverage opportunities and mitigate risks that arise in important contexts in practice: 1) when a team is composed of multiple humans with differential and potentially complementary abilities, 2) when the observational data includes consistent deterministic actions, and 3) when the covariate distribution of future decisions differ from that in the historical data. We demonstrate the effectiveness of our proposed methods using data on real human responses and semi-synthetic, and find that our methods offer reliable and advantageous performance across setting, and that it is superior to when either the algorithm or the AI make decisions on their own. We also find that the extensions we propose effectively improve the robustness of the human-AI collaboration performance in the presence of different challenging settings.

翻译：人类与AI的互补性在算法和人类均无法在特定情境的所有实例中占据主导地位时尤为重要。近期探索人机协作的研究主要关注分类任务中的决策问题。然而在许多人类可从AI互补性获益的重要场景中，人类需要采取行动方案。本文提出了一种新型人机协作框架，用于选择有利行动方案，即"学习人类-AI团队的互补策略"（\textsc{lcp-hai}）。我们的解决方案旨在通过路由模型将决策权分配给人类或AI，从而利用人机互补性最大化决策收益——该算法策略的目标是补充人类能力。我们进一步将方法扩展至实践中三个关键场景的风险机遇应对：1）团队由能力各异且可能互补的多个人类成员组成时；2）观测数据包含一致确定性动作时；3）未来决策的协变量分布与历史数据存在差异时。我们通过真实人类响应数据与半合成数据验证了方法的有效性，发现所提方法在不同场景下均能提供可靠且优越的性能，且优于任何单一决策者（算法或AI）的表现。同时，我们提出的扩展方案在应对不同挑战场景时，能有效提升人机协作性能的稳健性。