No Need for Learning to Defer? A Training Free Deferral Framework to Multiple Experts through Conformal Prediction

Abstract

AI systems often struggle to provide reliable predictions across all inputs, motivating hybrid human-AI decision-making. Existing Learning to Defer (L2D) approaches address this by training models to selectively defer to human experts. However, these approaches require extensive training data annotated by all experts and are sensitive to changes in expert composition, necessitating costly retraining. We propose a training-free, model- and expert-agnostic framework for expert deferral based on conformal prediction. Our method leverages prediction sets from a conformal predictor to quantify label-specific uncertainty and selects the most suitable expert using a segregativity criterion, which measures how well an expert discriminates among plausible labels. Experiments across three models on CIFAR10-H and HAM10000 demonstrate that our method can reduce the number of training labels per expert by up to 91.3% while maintaining predictive accuracy in low-data regimes. Being training-free, it also reduces training time by two orders of magnitude, offering a scalable, alternative to L2D for real-world human-AI collaboration.