I have started to serve as Associate Editor of IEEE Transactions on Reliability!
IEEE Transactions on Reliability is a leading peer-reviewed quarterly journal published by the IEEE Reliability Society. It focuses on quantitative methods, engineering theory, and practical applications that predict, measure, and improve the safety, maintainability, and dependability of hardware, software, and complex systems. Please find the editorial board of IEEE Transactions on Reliabilty below:
Prof. Xiaoge Zhang delivered a talk on “Uncertainty quantification for reliable AI and its applications to healthcare and traffic systems” at Tongji University, China
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.
Prof. Xingyu ZHAO from Wuhan University delivered a talk on “Deep Learning Robustness: From A System Reliability’s Perspective”
This talk examines deep learning robustness from a system reliability perspective, arguing that robustness should not be studied as an isolated AI model property but in relation to higher-level system claims such as safety assurance, reliability assessment, and security certification. It first reviews common robustness problem formulations, including binary robustness verification, maximum safe radius, maximum loss, and probabilistic robustness. Among these, probabilistic robustness is highlighted as especially relevant for safety-critical AI systems because it quantifies the likelihood of adversarial examples under stochastic perturbations, thereby aligning more naturally with reliability and risk reasoning in real-world operation.
The talk then distinguishes the roles of adversarial robustness and probabilistic robustness at system level. Adversarial robustness is more closely associated with security against maliciously optimized attacks, whereas probabilistic robustness better captures reliability and safety risks arising from benign operational noise, environmental uncertainty, and unsophisticated random perturbations. This distinction motivates a broader research agenda in which robustness metrics are selected according to the system-level property being claimed.
Building on this perspective, the talk presents the presenter team’s recent work on probabilistic robustness estimation, improvement and benchmarking. The talk advocates integrating probabilistic robustness into system-level safety and reliability modelling to support more meaningful assurance arguments for AI-enabled systems.
