Feature-Driven Priority Queuing

Rethinking Artificial Intelligence-Assisted Priority Queuing: The Cost of Locking in Classifiers When job types must be inferred from observable features, should artificial intelligence (AI) classifiers be locked before optimizing queue assignments, or should the full system be trained end to end? Singh, Gurvich, and Van Mieghem show that the modular “type-first” approach—locking a type classifier and then, optimizing queue assignment based only on its output distribution—can be fundamentally suboptimal. The authors establish that type-first achieves optimality only when the locked classifier recovers the Bayes posterior almost everywhere, a condition rarely satisfied in high-dimensional or misspecified settings. In contrast, their “direct” approach jointly optimizes feature-to-queue mappings to minimize empirical waiting costs and converges to the theoretical optimum. Using 100,000 chest X-rays from the National Institutes of Health, they show that direct optimization substantially improves waiting-cost performance and reveal mechanisms, such as statistical pooling and strategic overprioritization of medium-cost types. The work speaks to the growing debate over whether AI systems should be locked or adaptable.