Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

AI RMF - NIST Artifical Intelligence Risk Management Framework



In collaboration with the private and public sectors, NIST has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The NIST AI Risk Management Framework (AI RMF) is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. 

Released on January 26, 2023, the Framework was developed through a consensus-driven, open, transparent, and collaborative process that included a Request for Information, several draft versions for public comments, multiple workshops, and other opportunities to provide input. It is intended to build on, align with, and support AI risk management efforts by others. A companion NIST AI RMF Playbook also has been published by NIST along with an AI RMF Roadmap, AI RMF Crosswalk, and various Perspectives. In addition, NIST is making available a video explainer about the AI RMF. To view public comments received on the previous drafts of the AI RMF and Requests for Information, see the AI RMF Development page. 

NIST Standard Reference Data (SRD); ©Copyright 2023 by the U.S. Secretary of Commerce on behalf of the United States of America. All rights reserved.

The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.

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Tags:

  • accountability
  • robustness
  • privacy
  • Human-computer interaction
  • Security and resilience
  • safety

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.