As artificial intelligence (AI) is increasingly integrated into industrial applications, ensuring its reliability has become imperative, particularly in high-stakes decision-making settings. In this talk, I will present a general uncertainty quantification (UQ)-based framework for implementing reliable AI that proactively addresses both “known unknowns” and “unknown unknowns” that AI models inevitably encounter after deployment. Our proposed framework leverages the complementarity of model-based UQ methods (e.g., Monte Carlo dropout, variational inference) and model-agnostic, distribution-free conformal prediction (CP), together enabling AI models to explicitly express “I do not know” in situations where they are uncertain. In the UQ-based framework, model-based UQ methods are used to manage “unknown unknowns” by detecting inputs that lie outside the applicability domain (AD) of the trained models, thereby defining a region in which the assumption of data exchangeability is more plausible for conformal prediction. Within the model’s AD, conformal prediction is used to manage “known unknowns”, and provides finite-sample, distribution-free guarantees of model reliability by constructing prediction intervals that contain the true values with a user-specified coverage probability. I will use two case studies to demonstrate the practical utility of the proposed framework in supporting accurate and reliable pathology-based cancer diagnosis and traffic prediction over large-scale road networks.