D. Richard Kuhn, How Can We Provide Assured Autonomy?

Safety and security-critical systems require extensive test and evaluation, but existing high assurance test methods are based on structural coverage criteria that do not apply to many black box AI and machine learning components.   AI/ML systems make decisions based on training data rather than conventionally programmed functions.  Autonomous systems that rely on these components therefore require assurance methods that evaluate input data to ensure that they can function correctly in their environments with inputs they will encounter.  Combinatorial test methods can provide added assurance for these systems and complement conventional verification and test for AI/ML.This talk reviews some combinatorial methods that can be used to provide assured autonomy, including:Background on combinatorial test methodsWhy conventional test methods are not sufficient for many or most autonomous systemsWhere combinatorial methods applyAssurance based on input space coverageExplainable AI as part of validation About the speaker: Rick Kuhn is a computer scientist in the Computer Security Division at NIST, and is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He co-developed the role based access control (RBAC) model that is the dominant form of access control today. His current research focuses on combinatorial methods for assured autonomy and hardware security/functional verification. He has authored three books and more than 200 conference or journal publications on cybersecurity, software failure, and software verification and testing.