Information Technology
Overview
NIST aims to cultivate trust in the design, development, use and governance of Artificial Intelligence (AI) technologies and systems in ways that enhance safety and security and improve quality of life. NIST focuses on improving measurement science, technology, standards and related tools — including evaluation and data.
With AI and Machine Learning (ML) changing how society addresses challenges and opportunities, the trustworthiness of AI technologies is critical. Trustworthy AI systems are those demonstrated to be valid and reliable; safe, secure and resilient; accountable and transparent; explainable and interpretable; privacy-enhanced; and fair with harmful bias managed. The agency’s AI goals and activities are driven by its statutory mandates, Presidential Executive Orders and policies, and the needs expressed by U.S. industry, the global research community, other federal agencies,and civil society.
On October 30, 2023, President Biden signed an Executive Order (EO) to build U.S. capacity to evaluate and mitigate the risks of AI systems to ensure safety, security and trust, while promoting an innovative, competitive AI ecosystem that supports workers and protects consumers. Learn more about NIST's responsibilities in the EOand the creation of theU.S. Artificial Intelligence Safety Institute, including the new consortium that is being established.
NIST’s AI goals include:
- Conduct fundamental research to advance trustworthy AI technologies.
- Apply AI research and innovation across the NIST Laboratory Programs.
- Establish benchmarks, data and metrics to evaluate AI technologies.
- Lead and participate in development of technical AI standards.
- Contribute technical expertise to discussions and development of AI policies.
NIST’s AI efforts fall in several categories:
NIST’s AI portfolio includes fundamental research to advance the development of AI technologies — including software, hardware, architectures and the ways humans interact with AI technology and AI-generated information
AI approaches are increasingly an essential component in new research. NIST scientists and engineers use various machine learning and AI tools to gain a deeper understanding of and insight into their research. At the same time, NIST laboratory experiences with AI are leading to a better understanding of AI’s capabilities and limitations.
With a long history of working with the community to advance tools, standards and test beds, NIST increasingly is focusing on the sociotechnical evaluation of AI.
NIST leads and participates in the development of technical standards, including international standards, that promote innovation and public trust in systems that use AI. A broad spectrum of standards for AI data, performance and governance are a priority for the use and creation of trustworthy and responsible AI.
A fact sheet describes NIST's AI programs.
Featured Content
Executive Order on Safe, Secure, and Trustworthy AI
U.S. Artificial Intelligence Safety Institute
AI Risk Management Framework
AI Resource Center
Fundamental AI Research
Applied AI Research
AI Standards
AI Measurement and Evaluation
AI Policy Contributions
AI Engagement
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Artificial intelligence Topics
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The Research
Projects & Programs
Deep Learning for MRI Reconstruction and Analysis
Ongoing
Healthcare is big part of the national economy. Medical Imaging has emerged over the past several decades as a critical diagnosis and therapeutic tool. However
Emerging Hardware for Artificial Intelligence
Ongoing
Designing artificial intelligence (AI) from the device up could unlock improvements in critical metrics such as energy delay product and enable unique networks
Embodied AI and Data Generation for Manufacturing Robotics
Ongoing
Artificial intelligence (AI) in the form of advanced machine learning models has been widely adopted in the technology world today. These machine learning
Deep Generative Modeling for Communication Systems Testing and Data Sharing
Completed
This project aims to develop deep generative models, an emerging topic in AI, for radio frequency (RF) waveforms collected from real-world communications
JARVIS-ML
Ongoing
JARVIS-ML is a repository of machine learning (ML) model parameters, descriptors, and ML related input and target data. JARVIS-ML is a part of the NIST-JARVIS
View All Projects/Programs
Additional Resources Links
Publications
Software
NIST Launches Trustworthy and Responsible AI Resource Center (AIRC)
One-stop shop offers industry, government and academic stakeholders knowledge of AI standards, measurement methods and metrics, data sets, and other resources.
Learn More
News
Minimizing Harms and Maximizing the Potential of Generative AI
As generative AI tools like ChatGPT become more commonly used, we must think carefully about the impact on people and society.
Read more
NIST Reports First Results From Age Estimation Software Evaluation
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NIST Launches ARIA, a New Program to Advance Sociotechnical Testing and Evaluation for AI
Read more
U.S. Secretary of Commerce Gina Raimondo Releases Strategic Vision on AI Safety, Announces Plan for Global Cooperation Among AI Safety Institutes
Read more
Bias in AI
Psychology of Interpretable and Explainable AI
Events
2024 Artificial Intelligence for Materials Science (AIMS) Workshop
Wed, Jul 17 - Thu, Jul 18 2024
As a part of the JARVIS workshop series, NIST is sponsoring the 5th Artificial Intelligence for Materials Science (AIMS)
