主题：Targeted AI Deployment in Expert Decision-Making: A Dyadic Policy Learning Framework

专家决策中的针对性人工智能部署：一种双元策略学习框架

时间：2026年7月17日，下午15:30

地点：管理科研楼第二教室

主讲人:张成龙，复旦大学 副教授

Bio: 张成龙目前担任复旦大学管理学院信息系统与商业智能系副教授，博士生导师。其研究兴趣为生成式人工智能的部署问题，平台经济与信息产品的机制设计。其论文发表在Management Science，MIS Quarterly，Information Systems Research。其目前担任Decision Sciences的副编辑（AE）。

Abstract: Digital platforms increasingly use AI to augment, not replace, human experts, creating a need to target support based on heterogeneous expert–task interactions. Existing methods, which rely on i.i.d. assumptions and plug-in rules, often ignore first-stage estimation error and fail to quantify policy uncertainty. We introduce a dyadic policy learning framework that combines doubly robust estimation with two-way cross-fitting to address these limitations. Establishing uniform regret bounds under separate exchangeability, our approach uses a nested diagonal cross-fitting design and pigeonhole bootstrap for honest welfare evaluation. Applied to radiologists interpreting chest X-rays, our learned tree-based policies yield statistically significant welfare gains over no-AI baselines across diagnostic utility, ranking quality, and efficiency. By adapting policy learning to dyadic structures, we prevent the overfitting and biased precision estimates common in conventional i.i.d. methods, offering a robust blueprint for embedding AI in high-stakes, interdependent workflows.