Human-AI Collaborative Uncertainty Quantification

AI predictive systems increasingly support high-stakes decision making, yet robust decisions under uncertainty often rely on human capabilities beyond AI alone. This motivates collaborative approaches that combine human judgment with AI predictions. We study this problem through the lens of uncertainty quantification and introduce Human-AI Collaborative Uncertainty Quantification, a framework in which an AI system refines a human expert’s proposed prediction set subject to two principles: counterfactual harm, requiring that the AI not degrade correct human judgments, and complementarity, requiring recovery of correct outcomes the human missed. At the population level, we show that the optimal collaborative prediction set has a simple two-threshold structure over a single score function, governing pruning and augmentation relative to the human proposal. Building on this characterization, we develop offline and online calibration algorithms with distribution-free finite-sample guarantees. The online algorithm adapts to arbitrary distribution shifts, including settings where human behavior evolves through interaction with the AI. Empirically, we show that collaborative prediction sets outperform human-only and AI-only baselines, achieving improved coverage--efficiency tradeoffs across image classification, regression, and text-based medical decision making.