Models of negative dependence are increasingly important in machine learning. Whether selecting training data, finding an optimal experimental design, exploring in reinforcement learning, or making suggestions with recommender systems, selecting high-quality but diverse items has become a core challenge. This workshop aims to bring together researchers who, using theoretical or applied techniques, leverage negative dependence in their work. We will delve into the rich underlying mathematical theory, understand key applications, and discuss the most promising directions for future research.