Trustworthy Machine Learning: Fairness and Robustness

In recent years, machine learning (ML) technologies have experienced swift developments and attracted extensive attention from both academia and industry. The applications of ML are extended to multiple domains, from computer vision, text processing, to recommendations, etc. However, recent studies have uncovered the untrustworthy side of ML applications. For example, ML algorithms could show human-like discrimination against certain individuals or groups, or make unreliable decisions in safety-critical scenarios, which implies the absence of fairness and robustness, respectively. Consequently, building trustworthy machine learning systems has become an urgent need. My research strives to help meet this demand. In particular, my research focuses on designing trustworthy ML models and spans across three main areas: (1) fairness in ML, where we aim to detect, eliminate bias and ensure fairness in various ML applications; (2) robustness in ML, where we seek to ensure the robustness of certain ML applications towards adversarial attacks; (3) specific applications of ML, where my research involves the development of ML-based natural language processing (NLP) models and recommendation systems.