Default Policy

Default policies, encompassing pre-set behaviors or constraints in various systems, are a growing area of research focusing on improving efficiency, fairness, and safety. Current work investigates optimal default settings for machine learning models, including multilayer perceptrons and gradient-boosted decision trees, aiming to balance performance and computational cost without extensive hyperparameter tuning. This research is crucial for advancing the development of robust and reliable AI systems, particularly in applications like foundation models where acceptable use policies and fairness considerations are paramount. Furthermore, understanding default policies is vital for designing efficient and equitable multi-task learning algorithms and improving user experience in interactive systems like voice assistants.