Fostering Trustworthiness in Machine Learning Algorithms

Abstract Recent years have seen a surge in research that develops and applies machine learning algorithms to create intelligent learning systems. However, traditional machine learning algorithms have primarily focused on optimizing accuracy and efficiency, and they often fail to consider how to foster trustworthiness in their design. As a result, machine learning models usually face a trust crisis in real-world applications. Driven by these urgent concerns about trustworthiness, in this talk, I will introduce my research efforts towards the goal of making machine learning trustworthy. Specifically, I will delve into the following key research topics: security vulnerabilities and robustness, model explanations, and privacy-preserving mechanisms.