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Overview

NIST’s AI portfolio includes fundamental research, development, and standards for AI technologies – including software, hardware, architectures, human- interaction and teaming, and all relevant intersections and interfaces – vital for AI computational trust.

Establishing and Promoting Technical Requirements for Trustworthy and Responsible AI

  • Through targeted research investments, NIST identifies and quantifies trustworthy and responsible AI in technical terms and develops tools and guidance so that designers, developers, and evaluators can take appropriate actions. These include developing taxonomy, terminology, and testbeds for measurements of AI risks – as well as standards needed for key technical characteristics of AI trustworthiness. They address accuracy, explainability and interpretability, privacy, reliability, robustness, safety, security (resilience), and harmful bias mitigation.
  • By convening stakeholder workshops and via other engagements, NIST is developing a resource center of documents, software, standards, and related tools that contribute to better understanding, identifying, measuring, and managing various risks associated with AI systems.
  • Reflecting private sector interest and as mandated by Congress, NIST is developing a voluntary AI risk management framework through collaboration with stakeholders across public and private sectors.

Hardware for AI: Creating New Measurements and Technical Approaches for New AI Chips

  • Demand for faster, more energy-efficient information processing is growing exponentially as AI becomes more prevalent in our everyday lives. Conventional digital processing hardware cannot keep up with this demand. That is why researchers, taking inspiration from the brain, are considering alternatives where massively connected networks of artificial neurons and synapses process information with high speed, energy efficiency, scalability, and adaptive learning capabilities. 
  • To that end, NIST is helping to develop devices, circuits, systems, measurements, and theory to support the evolution of AI hardware technology from laboratory research to commercial applications. It is focusing on scalability, energy efficiency, hardware optimization, and architecture development. The new hardware leverages the physics of devices to perform computations, while the architectures and algorithms have entirely new intelligent functionality. This calls for a new system of measurement techniques and protocols.
    For more information about this work, see Hardware for AI