Research
151d agoSurveying Uncertainty in Explainable AI
This global survey examines uncertainty-aware explainable AI, highlighting how uncertainty is woven into explanations and evaluated. It identifies three main quantification methods—Bayesian, Monte Carlo, and Conformal—and three integration strategies: trust assessment, model constraints, and explicit communication.
The Signal
Fragmented evaluation practices underscore a need for standardized, user‑centric reliability metrics worldwide across domains.