Robustness and Reliability of Machine Learning Systems: A Comprehensive Review
Abstract
Yifei Wang
Machine learning systems have become an integral part of modern-day technology, driving advancements in various fields such as healthcare, finance, and autonomous systems. However, the robustness and reliability of these systems are crucial to their safe and effective deployment. In this paper, we present a comprehensive review of the current state of research in the robustness and reliability of machine learning systems, focusing on the challenges, potential solutions, and future directions in this area. We discuss the importance of adversarial attacks, dataset shift, and model interpretability in assessing the robustness of machine learning systems, as well as various approaches to improve their reliability, such as regularization, data augmentation, and ensemble learning. We conclude with a discussion of future research directions and open challenges in this field.