Targeted AI Deployment in Expert Decision-Making: A Dyadic Policy Learning Framework
题目：Targeted AI Deployment in Expert Decision-Making: A Dyadic Policy Learning Framework
主讲人：张成龙 副教授 信息管理与商业智能系
时间：2026-09-29 13:30-14:30
地点：
史代楼410室
内容摘要:
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.
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