SOURCE-LINKED INTELLIGENCE

Cost-Aware Post-Hoc Deferral Under Calibration and Shift: An Environmental AI Case Study

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Choosing a deferral policy for a frozen classifier requires more than ranking uncertain cases: confidence may be miscalibrated, errors have unequal costs, reviewers can err, and deployment data can leave calibration support. We study these interactions through EcoTrust, a post-hoc framework that compares automatic action with review using a six-group error-risk estimator, class-asymmetric costs, reviewer accuracy, and an optional support gate. On a Columbia River thermal-stress testbed, the learned estimator improves error-ranking area under the receiver operating characteristic curve from 0.8

Read original source ↗ Open in workspace

recordType

paper

region

Global