Speaker: Associate Professor Chenglong Zhang, Department of Information Management and Business Intelligence

Time: 13:30-14:30, Tuesday, September 29, 2026

Venue: Guoshun Campus, Room 410, Starr Building

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.