Artificial Intelligence Risk Management Framework 1.0

The Artificial Intelligence Risk Management Framework (AI RMF 1.0) provides organizations with a structured approach to managing risks associated with AI systems. It outlines essential functions such as governance, mapping, measuring, and managing AI risks to enhance trustworthiness and accountability. This framework is designed for AI actors across various sectors, ensuring that AI technologies are developed and deployed responsibly. Key concepts include understanding AI risks, establishing risk tolerance, and integrating stakeholder perspectives throughout the AI lifecycle. The AI RMF serves as a vital resource for organizations aiming to align AI practices with ethical and legal standards.

Key Points

Defines risk management strategies for AI systems across sectors.
Outlines governance, mapping, measuring, and managing functions.
Emphasizes stakeholder engagement and interdisciplinary collaboration.
Addresses unique risks associated with AI technologies and data.

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48 pages

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48 pages

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NIST AI 100-1

Artificial Intelligence Risk Management

Framework (AI RMF 1.0)

NIST AI 100-1

Artificial Intelligence Risk Management

Framework (AI RMF 1.0)

This publication is available free of charge from:

https://doi.org/10.6028/NIST.AI.100-1

January 2023

U.S. Department of Commerce

Gina M. Raimondo, Secretary

National Institute of Standards and Technology

Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology

Certain commercial entities, equipment, or materials may be identified in this document in order to describe

an experimental procedure or concept adequately. Such identification is not intended to imply recommenda-

tion or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that

the entities, materials, or equipment are necessarily the best available for the purpose.

This publication is available free of charge from: https://doi.org/10.6028/NIST.AI.100-1

Update Schedule and Versions

The Artificial Intelligence Risk Management Framework (AI RMF) is intended to be a living document.

NIST will review the content and usefulness of the Framework regularly to determine if an update is appro-

priate; a review with formal input from the AI community is expected to take place no later than 2028. The

Framework will employ a two-number versioning system to track and identify major and minor changes. The

first number will represent the generation of the AI RMF and its companion documents (e.g., 1.0) and will

change only with major revisions. Minor revisions will be tracked using “.n” after the generation number

(e.g., 1.1). All changes will be tracked using a Version Control Table which identifies the history, including

version number, date of change, and description of change. NIST plans to update the AI RMF Playbook

frequently. Comments on the AI RMF Playbook may be sent via email to AIframework@nist.gov at any time

and will be reviewed and integrated on a semi-annual basis.

Overview

Artificial Intelligence Risk Management Framework 1.0

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FAQs of Artificial Intelligence Risk Management Framework 1.0

What are the core functions of the AI RMF?

The core functions of the AI RMF include governance, mapping, measuring, and managing AI risks. Governance involves establishing a culture of risk management and aligning AI practices with organizational values. Mapping focuses on understanding the context and identifying potential risks associated with AI systems. Measuring assesses the effectiveness of AI systems and their trustworthiness, while managing involves prioritizing risks and implementing strategies to mitigate them. Together, these functions provide a comprehensive framework for organizations to ensure responsible AI development and deployment.

How does the AI RMF address unique AI risks?

The AI RMF identifies unique risks that arise from the use of AI technologies, such as data bias, model opacity, and the complexity of AI systems. It emphasizes the importance of understanding the context in which AI systems operate and the potential impacts on individuals and society. By incorporating stakeholder perspectives and interdisciplinary collaboration, the framework aims to mitigate these risks effectively. Additionally, it provides guidelines for monitoring and evaluating AI systems to ensure they remain aligned with ethical and legal standards throughout their lifecycle.

Who are the intended users of the AI RMF?

The intended users of the AI RMF include organizations designing, developing, deploying, and evaluating AI systems. This encompasses a wide range of AI actors, such as data scientists, engineers, project managers, and compliance officers. The framework is also relevant for policymakers and stakeholders interested in the ethical implications of AI technologies. By providing a structured approach to risk management, the AI RMF serves as a valuable resource for anyone involved in the AI lifecycle, ensuring that AI practices are responsible and trustworthy.

What is the significance of stakeholder engagement in the AI RMF?

Stakeholder engagement is crucial in the AI RMF as it ensures that diverse perspectives are considered in the risk management process. Involving stakeholders helps organizations identify potential risks and impacts that may not be apparent from a technical standpoint alone. This collaborative approach fosters transparency and accountability, enhancing the trustworthiness of AI systems. By integrating feedback from affected communities and experts, organizations can make informed decisions that align with societal values and expectations, ultimately leading to more responsible AI practices.

What challenges does the AI RMF address in AI risk management?

The AI RMF addresses several challenges in AI risk management, including the difficulty of measuring AI risks and the evolving nature of AI technologies. It highlights the need for organizations to establish clear risk tolerance levels and prioritize risks based on their potential impacts. Additionally, the framework emphasizes the importance of continuous monitoring and adaptation to emerging risks as AI systems evolve. By providing a structured approach, the AI RMF aims to help organizations navigate these complexities and implement effective risk management strategies.