Cost-Aware Deferral for Classifiers Under Calibration Shift: Environmental AI Case Study

A new arXiv preprint examines how to choose a deferral policy for a fixed classifier, where uncertain cases are routed to human reviewers. The authors analyze how miscalibrated confidence scores, unequal error costs, fallible reviewers, and deployment-time distribution shift interact, using an environmental AI application as a real-world case study. The work offers practical guidance for deciding when automated predictions should be handed off rather than trusted outright.