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AI Foundational Research - Explainability

A multidisciplinary team of computer scientists, cognitive scientists, mathematicians, and specialists in AI and machine learning that all have diverse background and research specialties, explore and define the core tenets of explainable AI (XAI). The team aims to develop measurement methods and best practices that support the implementation of those tenets. Ultimately, the team plans to develop a metrologist’s guide to AI systems that address the complex entanglement of terminology and taxonomy as it relates to the myriad layers of the AI field. AI must be explainable to society to enable understanding, trust, and adoption of new AI technologies, the decisions produced, or guidance provided by AI systems.

A NIST Internal Report on XAI is planned for release for public comment in June 2020. The report will present a multidisciplinary overview that places existing work in the context of four key principles and summarizes the state-of-practice in explainable AI. 

Created April 6, 2020, Updated May 12, 2020