AI RMF
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AI RMF Resources
AI RMF
Executive Summary
Framing Risk
Audience
AI Risks and Trustworthiness
Effectiveness of the AI RMF
AI RMF Core
AI RMF Profiles
📚 App. A: Descriptions of AI Actor Tasks
📚 App. B: How AI Risks Differ from Traditional Software Risks
📚 App. C: AI Risk Management and Human-AI Interaction
📚 App. D: Attributes of the AI RMF
Playbook
Roadmap
Example of Use Cases
Crosswalk Documents
Glossary
Technical Reports
AI Risk Management Framework
The 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.
As a consensus resource, the AI RMF was developed in an open, transparent, multidisciplinary, and multistakeholder
manner over an 18-month time period and in collaboration with more than 240 contributing organizations from private
industry, academia, civil society, and government. Feedback received during the development of the AI RMF is
publicly available
on the NIST website
Download the framework
Framing risk
Framing risk
includes information on:
Understanding and Addressing Risks, Impacts, and Harms
Challenges for AI Risk Management
Audience
Identifying and managing AI risks and potential impacts requires a broad set of perspectives and actors
across the AI lifecycle. The
Audience
section describes AI actors and the AI lifecycle.
AI Risks and Trustworthiness
For AI systems to be trustworthy, they often need to be responsive to a multiplicity of criteria that are
of value to interested parties. Approaches which enhance AI trustworthiness can reduce negative AI risks.
The
AI Risks and Trustworthiness
section articulates the characteristics of trustworthy AI and offers guidance for addressing them.
Effectiveness of the AI RMF
The
Effectiveness
section describes expected benefits for users of the framework.
AI RMF Core
The
AI RMF Core
provides outcomes and actions that enable dialogue, understanding, and activities to manage AI risks and
responsibility develop trustworthy AI systems. This is operationalized through four functions: Govern,
Map, Measure, and Manage.
AI RMF Profiles
The use-case
Profiles
are
implementations of the AI RMF functions, categories, and subcategories for a specific setting or
application based on the requirements, risk tolerance, and resources of the Framework user.
Appendix A:
Descriptions of AI Actor Tasks
Appendix B:
How AI Risks Differ from Traditional Software Risks
Appendix C:
AI Risk Management and Human-AI Interaction
Appendix D:
Attributes of the AI RMF