Trustworthy Machine Learning

Trustworthy machine learning (TML) aims to build and deploy machine learning models that are reliable, robust, and fair, addressing concerns about accuracy, bias, and explainability. Current research focuses on improving model robustness against adversarial attacks and distributional shifts, enhancing interpretability through techniques like Shapley value analysis and knowledge distillation, and developing methods for quantifying and managing uncertainty. These advancements are crucial for increasing the adoption of AI in high-stakes applications like healthcare and finance, where trust in model outputs is paramount.