Abstract: Disclosed are techniques, and related systems, for developing and implementing a unified framework for quantifying risk in machine learning models, such as deep neural networks. The framework can be easy-to-use and flexible to apply to different types of machine learning models. The framework can be used to automatically assess different forms of risk in parallel, including but not limited to aleatoric uncertainty, epistemic uncertainty, and/or vacuitic uncertainty. To that end, the disclosed framework provides wrappers that compose different automatic risk assessment algorithms. The obtained risk estimates can be used, for example, for: providing a deeper insight into decision boundaries of neural networks; performing downstream tasks by integrating the risk estimates back into a learning lifecycle for the model to improve robustness and generalization; and/or improving safety by identifying potential model failures based on the risk values.