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Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022
The Robust Artificial Intelligence System Assurance (RAISA) workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems.
Tools and Practices for Responsible AI Engineering
Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability - represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits.
Principles for Evaluation of AI/ML Model Performance and Robustness
The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs.
Fast Training of Deep Neural Networks Robust to Adversarial Perturbations
Recent work in adversarial training, a form of robust optimization in which the model is optimized against adversarial examples, demonstrates the ability to improve performance sensitivities to perturbations and yield feature representations that are more interpretable.
Safe Predictors for Enforcing Input-Output Specifications
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training.
Kernelized Capsule Networks
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships.
