Silent Failure

Silent failures, the undetected errors in systems ranging from medical image classifiers to robotic manipulators and computer systems, pose a significant challenge across diverse fields. Current research focuses on developing robust methods for detecting these failures, employing techniques like ensemble methods integrating machine learning algorithms (e.g., LSTMs, support vector machines) and causal Bayesian networks to identify error causes and predict potential failures. This work aims to improve system reliability and safety by enhancing failure detection and prevention capabilities, ultimately leading to more trustworthy and robust applications in healthcare, robotics, and computing.