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AI-100-1/ai_rmf_subcategory/govern-1.1
GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented.
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
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2023-01
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AI RMF Core > Govern > GOVERN 1.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-1.2
GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.
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AI RMF Core > Govern > GOVERN 1.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-1.3
GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance.
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AI RMF Core > Govern > GOVERN 1.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-1.4
GOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities. Categories Subcategories Continued on next page Table 1: Categories and subcategories for the GOVERN function. (Continued)
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AI RMF Core > Govern > GOVERN 1.4
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-1.5
GOVERN 1.5: Ongoing monitoring and periodic review of the risk management process and its outcomes are planned and organizational roles and responsibilities clearly defined, including determining the frequency of periodic review.
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AI RMF Core > Subcategories > GOVERN 1.5
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-1.6
GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities.
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AI RMF Core > Subcategories > GOVERN 1.6
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-1.7
GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness. GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, ...
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
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ai_rmf_subcategory
null
AI RMF Core > Subcategories > GOVERN 1.7
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-2.1
GOVERN 2.1: Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization.
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null
AI RMF Core > Subcategories > GOVERN 2.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-2.2
GOVERN 2.2: The organization's personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements.
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AI RMF Core > Subcategories > GOVERN 2.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-2.3
GOVERN 2.3: Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment. GOVERN 3: Workforce diversity, equity, inclusion, and accessibility processes are prioritized in the mapping, measuring, and managing of AI risks throughout the lifec...
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AI RMF Core > Subcategories > GOVERN 2.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-3.1
GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team (e.g., diversity of demographics, disciplines, experience, expertise, and backgrounds).
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AI RMF Core > Subcategories > GOVERN 3.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-3.2
GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems. GOVERN 4: Organizational teams are committed to a culture
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AI RMF Core > Subcategories > GOVERN 3.2
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-4.1
GOVERN 4.1: Organizational policies and practices are in place to foster a critical thinking and safety-first mindset in the design, development, deployment, and uses of AI systems to minimize potential negative impacts. Categories Subcategories Continued on next page Table 1: Categories and subcategories for the GOVER...
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AI RMF Core > Subcategories > GOVERN 4.1
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-4.2
GOVERN 4.2: Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate, and use, and they communicate about the impacts more broadly.
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AI RMF Core > Subcategories > GOVERN 4.2
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AI-100-1/ai_rmf_subcategory/govern-4.3
GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing. GOVERN 5: Processes are in place for robust engagement with relevant AI actors.
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AI RMF Core > Subcategories > GOVERN 4.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-5.1
GOVERN 5.1: Organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback from those external to the team that developed or deployed the AI system regarding the potential individual and societal impacts related to AI risks.
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AI RMF Core > Subcategories > GOVERN 5.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-5.2
GOVERN 5.2: Mechanisms are established to enable the team that developed or deployed AI systems to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation. GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software a...
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ai_rmf_subcategory
null
AI RMF Core > Subcategories > GOVERN 5.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/ai_rmf_subcategory/govern-6.1
GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party's intellectual property or other rights.
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AI RMF Core > Subcategories > GOVERN 6.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/govern-6.2
GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk. Categories Subcategories 5.2 Map The MAP function establishes the context to frame risks related to an AI system. The AI lifecycle consists of many interdependent activities involving...
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AI RMF Core > Subcategories > GOVERN 6.2
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-1.1
MANAGE 1.1: A determination is made as to whether the AI system achieves its intended purposes and stated objectives and whether its development or deployment should proceed.
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AI RMF Core > Manage > MANAGE 1.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-1.2
MANAGE 1.2: Treatment of documented AI risks is prioritized based on impact, likelihood, and available resources or methods.
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AI RMF Core > Manage > MANAGE 1.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-1.3
MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented. Risk response options can include mitigating, transferring, avoiding, or accepting.
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AI RMF Core > Manage > MANAGE 1.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-1.4
MANAGE 1.4: Negative residual risks (defined as the sum of all unmitigated risks) to both downstream acquirers of AI systems and end users are documented. MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by input from relevant AI act...
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AI RMF Core > Manage > MANAGE 1.4
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-2.1
MANAGE 2.1: Resources required to manage AI risks are taken into account - along with viable non-AI alternative systems, approaches, or methods - to reduce the magnitude or likelihood of potential impacts.
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AI RMF Core > Manage > MANAGE 2.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-2.2
MANAGE 2.2: Mechanisms are in place and applied to sustain the value of deployed AI systems.
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AI RMF Core > Manage > MANAGE 2.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-2.3
MANAGE 2.3: Procedures are followed to respond to and recover from a previously unknown risk when it is identified.
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AI RMF Core > Manage > MANAGE 2.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-2.4
MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use. MANAGE 3: AI risks and benefits from third-party entities are managed.
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AI RMF Core > Manage > MANAGE 2.4
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-3.1
MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented.
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AI RMF Core > Manage > MANAGE 3.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/manage-3.2
MANAGE 3.2: Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance. Categories Subcategories Continued on next page Table 4: Categories and subcategories for the MANAGE function. (Continued) MANAGE 4: Risk treatments, including response and recovery, and ...
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AI RMF Core > Manage > MANAGE 3.2
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AI-100-1/ai_rmf_subcategory/manage-4.1
MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management.
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AI RMF Core > Subcategories > MANAGE 4.1
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AI-100-1/ai_rmf_subcategory/manage-4.2
MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular engagement with interested parties, including relevant AI actors.
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AI RMF Core > Subcategories > MANAGE 4.2
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AI-100-1/ai_rmf_subcategory/manage-4.3
MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented. Categories Subcategories 6.
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AI RMF Core > Subcategories > MANAGE 4.3
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/map-1.1
MAP 1.1: Intended purposes, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: the specific set or types of users along with their expectations; potential positive and negativ...
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AI RMF Core > Map > MAP 1.1
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AI-100-1/ai_rmf_subcategory/map-1.2
MAP 1.2: Interdisciplinary AI actors, competencies, skills, and capacities for establishing context reflect demographic diversity and broad domain and user experience expertise, and their participation is documented. Opportunities for interdisciplinary collaboration are prioritized.
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AI RMF Core > Map > MAP 1.2
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AI-100-1/ai_rmf_subcategory/map-1.3
MAP 1.3: The organization's mission and relevant goals for AI technology are understood and documented.
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AI RMF Core > Map > MAP 1.3
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AI-100-1/ai_rmf_subcategory/map-1.4
MAP 1.4: The business value or context of business use has been clearly defined or - in the case of assessing existing AI systems - re-evaluated.
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AI RMF Core > Map > MAP 1.4
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AI-100-1/ai_rmf_subcategory/map-1.6
MAP 1.6: System requirements (e.g., "the system shall respect the privacy of its users") are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks. MAP 2: Categorization of the AI system is performed.
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AI RMF Core > Map > MAP 1.6
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AI-100-1/ai_rmf_subcategory/map-2.1
MAP 2.1: The specific tasks and methods used to implement the tasks that the AI system will support are defined (e.g., classifiers, generative models, recommenders).
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AI RMF Core > Map > MAP 2.1
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AI-100-1/ai_rmf_subcategory/map-2.2
MAP 2.2: Information about the AI system's knowledge limits and how system output may be utilized and overseen by humans is documented. Documentation provides sufficient information to assist relevant AI actors when making decisions and taking subsequent actions. Categories Subcategories Continued on next page Table 2:...
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AI RMF Core > Map > MAP 2.2
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AI-100-1/ai_rmf_subcategory/map-2.3
MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct validation. MAP 3: AI capabilities, targeted usage, goals, and e...
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ai_rmf_subcategory
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AI RMF Core > Subcategories > MAP 2.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/ai_rmf_subcategory/map-3.1
MAP 3.1: Potential benefits of intended AI system functionality and performance are examined and documented.
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AI RMF Core > Subcategories > MAP 3.1
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/map-3.2
MAP 3.2: Potential costs, including non-monetary costs, which result from expected or realized AI errors or system functionality and trustworthiness - as connected to organizational risk tolerance - are examined and documented.
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AI RMF Core > Subcategories > MAP 3.2
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7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/map-3.3
MAP 3.3: Targeted application scope is specified and documented based on the system's capability, established context, and AI system categorization.
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AI RMF Core > Subcategories > MAP 3.3
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AI-100-1/ai_rmf_subcategory/map-3.4
MAP 3.4: Processes for operator and practitioner proficiency with AI system performance and trustworthiness - and relevant technical standards and certifications - are defined, assessed, and documented.
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AI RMF Core > Subcategories > MAP 3.4
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AI-100-1/ai_rmf_subcategory/map-3.5
MAP 3.5: Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function. MAP 4: Risks and benefits are mapped for all components of the AI system including third-party software and data.
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ai_rmf_subcategory
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AI RMF Core > Subcategories > MAP 3.5
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AI-100-1/ai_rmf_subcategory/map-4.1
MAP 4.1: Approaches for mapping AI technology and legal risks of its components - including the use of third-party data or software - are in place, followed, and documented, as are risks of infringement of a third party's intellectual property or other rights.
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AI RMF Core > Subcategories > MAP 4.1
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AI-100-1/ai_rmf_subcategory/map-4.2
MAP 4.2: Internal risk controls for components of the AI system, including third-party AI technologies, are identified and documented. MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized.
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ai_rmf_subcategory
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AI RMF Core > Subcategories > MAP 4.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/ai_rmf_subcategory/map-5.1
MAP 5.1: Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documente...
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
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ai_rmf_subcategory
null
AI RMF Core > Subcategories > MAP 5.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/map-5.2
MAP 5.2: Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented. Categories Subcategories 5.3 Measure The MEASURE function employs quantitative, qualitative, or mixed-method tools, techniq...
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ai_rmf_subcategory
null
AI RMF Core > Subcategories > MAP 5.2
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/measure-1.1
MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks. The risks or trustworthiness characteristics that will not - or cannot - be measured are properly documented.
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ai_rmf_subcategory
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AI RMF Core > Measure > MEASURE 1.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/ai_rmf_subcategory/measure-1.2
MEASURE 1.2: Appropriateness of AI metrics and effectiveness of existing controls are regularly assessed and updated, including reports of errors and potential impacts on affected communities.
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ai_rmf_subcategory
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AI RMF Core > Measure > MEASURE 1.2
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AI-100-1/ai_rmf_subcategory/measure-1.3
MEASURE 1.3: Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates. Domain experts, users, AI actors external to the team that developed or deployed the AI system, and affected communities are consulted in support of asses...
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ai_rmf_subcategory
null
AI RMF Core > Measure > MEASURE 1.3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/ai_rmf_subcategory/measure-2.1
MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented.
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ai_rmf_subcategory
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AI RMF Core > Measure > MEASURE 2.1
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/ai_rmf_subcategory/measure-2.10
MEASURE 2.10: Privacy risk of the AI system - as identified in the MAP function - is examined and documented.
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ai_rmf_subcategory
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AI RMF Core > Subcategories > MEASURE 2.10
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/ai_rmf_subcategory/measure-2.11
MEASURE 2.11: Fairness and bias - as identified in the MAP function - are evaluated and results are documented.
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AI RMF Core > Subcategories > MEASURE 2.11
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AI-100-1/ai_rmf_subcategory/measure-2.12
MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities - as identified in the MAP function - are assessed and documented.
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ai_rmf_subcategory
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AI RMF Core > Subcategories > MEASURE 2.12
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AI-100-1/ai_rmf_subcategory/measure-2.13
MEASURE 2.13: Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented. MEASURE 3: Mechanisms for tracking identified AI risks over time are in place.
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AI RMF Core > Subcategories > MEASURE 2.13
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AI-100-1/ai_rmf_subcategory/measure-2.2
MEASURE 2.2: Evaluations involving human subjects meet applicable requirements (including human subject protection) and are representative of the relevant population.
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ai_rmf_subcategory
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AI RMF Core > Measure > MEASURE 2.2
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AI-100-1/ai_rmf_subcategory/measure-2.3
MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented.
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ai_rmf_subcategory
null
AI RMF Core > Measure > MEASURE 2.3
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AI-100-1/ai_rmf_subcategory/measure-2.4
MEASURE 2.4: The functionality and behavior of the AI system and its components - as identified in the MAP function - are monitored when in production.
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ai_rmf_subcategory
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AI RMF Core > Measure > MEASURE 2.4
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AI-100-1/ai_rmf_subcategory/measure-2.5
MEASURE 2.5: The AI system to be deployed is demonstrated to be valid and reliable. Limitations of the generalizability beyond the conditions under which the technology was developed are documented. Categories Subcategories Continued on next page Table 3: Categories and subcategories for the MEASURE function. (Continue...
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ai_rmf_subcategory
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AI RMF Core > Measure > MEASURE 2.5
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AI-100-1/ai_rmf_subcategory/measure-2.6
MEASURE 2.6: The AI system is evaluated regularly for safety risks - as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits. Safety metr...
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ai_rmf_subcategory
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AI RMF Core > Subcategories > MEASURE 2.6
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AI-100-1/ai_rmf_subcategory/measure-2.7
MEASURE 2.7: AI system security and resilience - as identified in the MAP function - are evaluated and documented.
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AI RMF Core > Subcategories > MEASURE 2.7
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AI-100-1/ai_rmf_subcategory/measure-2.8
MEASURE 2.8: Risks associated with transparency and accountability - as identified in the MAP function - are examined and documented.
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AI RMF Core > Subcategories > MEASURE 2.8
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AI-100-1/ai_rmf_subcategory/measure-2.9
MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context - as identified in the MAP function - to inform responsible use and governance.
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AI RMF Core > Subcategories > MEASURE 2.9
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AI-100-1/ai_rmf_subcategory/measure-3.1
MEASURE 3.1: Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts.
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AI RMF Core > Subcategories > MEASURE 3.1
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AI-100-1/ai_rmf_subcategory/measure-3.2
MEASURE 3.2: Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available. Categories Subcategories Continued on next page Table 3: Categories and subcategories for the MEASURE function. (Continued)
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AI RMF Core > Subcategories > MEASURE 3.2
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AI-100-1/ai_rmf_subcategory/measure-3.3
MEASURE 3.3: Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics. MEASURE 4: Feedback about efficacy of measurement is gathered and assessed.
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AI RMF Core > Subcategories > MEASURE 3.3
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AI-100-1/ai_rmf_subcategory/measure-4.1
MEASURE 4.1: Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented.
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AI RMF Core > Subcategories > MEASURE 4.1
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AI-100-1/ai_rmf_subcategory/measure-4.2
MEASURE 4.2: Measurement results regarding AI system trustworthiness in deployment context(s) and across the AI lifecycle are informed by input from domain experts and relevant AI actors to validate whether the system is performing consistently as intended. Results are documented.
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AI RMF Core > Subcategories > MEASURE 4.2
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AI-100-1/ai_rmf_subcategory/measure-4.3
MEASURE 4.3: Measurable performance improvements or declines based on consultations with relevant AI actors, including affected communities, and field data about contextrelevant risks and trustworthiness characteristics are identified and documented. Categories Subcategories 5.4 Manage The MANAGE function entails alloc...
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ai_rmf_subcategory
null
AI RMF Core > Subcategories > MEASURE 4.3
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AI-100-1/section/accountable-and-transparent
Accountable and Transparent Trustworthy AI depends upon accountability. Accountability presupposes transparency. Transparency reflects the extent to which information about an AI system and its outputs is available to individuals interacting with such a system - regardless of whether they are even aware that they are ...
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section
null
AI Risks and Trustworthiness > Accountable and Transparent
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AI-100-1/section/additional-ai-actors
Additional AI Actors Third-party entities include providers, developers, vendors, and evaluators of data, al- gorithms, models, and/or systems and related services for another organization or the or- ganization's customers or clients. Third-party entities are responsible for AI design and development tasks, in whole o...
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section
null
Appendix A: > Descriptions of AI Actor Tasks from Figures 2 and 3 > Additional AI Actors
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AI-100-1/section/ai-risk-management-and-human-ai-interaction
AI Risk Management and Human-AI Interaction Organizations that design, develop, or deploy AI systems for use in operational settings may enhance their AI risk management by understanding current limitations of human- AI interaction. The AI RMF provides opportunities to clearly define and differentiate the various huma...
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section
null
Appendix C: > AI Risk Management and Human-AI Interaction
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/ai-risks-and-trustworthiness
AI Risks and Trustworthiness For AI systems to be trustworthy, they often need to be responsive to a multiplicity of cri- teria that are of value to interested parties. Approaches which enhance AI trustworthiness can reduce negative AI risks. This Framework articulates the following characteristics of trustworthy AI a...
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section
null
AI Risks and Trustworthiness
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/ai-rmf-core
AI RMF Core The AI RMF Core provides outcomes and actions that enable dialogue, understanding, and activities to manage AI risks and responsibly develop trustworthy AI systems. As illus- trated in Figure 5, the Core is composed of four functions: GOVERN, MAP, MEASURE, and MANAGE. Each of these high-level functions is ...
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section
null
AI RMF Core
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/attributes-of-the-ai-rmf
Attributes of the AI RMF NIST described several key attributes of the AI RMF when work on the Framework first began. These attributes have remained intact and were used to guide the AI RMF's devel- opment. They are provided here as a reference. The AI RMF strives to: 1. Be risk-based, resource-efficient, pro-innovatio...
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section
null
Appendix D: > Attributes of the AI RMF
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/audience
Audience Identifying and managing AI risks and potential impacts - both positive and negative - re- quires a broad set of perspectives and actors across the AI lifecycle. Ideally, AI actors will represent a diversity of experience, expertise, and backgrounds and comprise demograph- ically and disciplinarily diverse te...
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section
null
Audience
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/descriptions-of-ai-actor-tasks-from-figures-2-and-3
Descriptions of AI Actor Tasks from Figures 2 and 3 AI Design tasks are performed during the Application Context and Data and Input phases of the AI lifecycle in Figure 2. AI Design actors create the concept and objectives of AI systems and are responsible for the planning, design, and data collection and processing t...
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section
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Appendix A: > Descriptions of AI Actor Tasks from Figures 2 and 3
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/effectiveness-of-the-ai-rmf
Effectiveness of the AI RMF Evaluations of AI RMF effectiveness - including ways to measure bottom-line improve- ments in the trustworthiness of AI systems - will be part of future NIST activities, in conjunction with the AI community. Organizations and other users of the Framework are encouraged to periodically evalu...
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Effectiveness of the AI RMF
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/executive-summary
Executive Summary Artificial intelligence (AI) technologies have significant potential to transform society and people's lives - from commerce and health to transportation and cybersecurity to the envi- ronment and our planet. AI technologies can drive inclusive economic growth and support scientific advancements that...
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Executive Summary
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/explainable-and-interpretable
Explainable and Interpretable Explainability refers to a representation of the mechanisms underlying AI systems' oper- ation, whereas interpretability refers to the meaning of AI systems' output in the context of their designed functional purposes. Together, explainability and interpretability assist those operating o...
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AI Risks and Trustworthiness > Explainable and Interpretable
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/fair-with-harmful-bias-managed
Fair - with Harmful Bias Managed Fairness in AI includes concerns for equality and equity by addressing issues such as harm- ful bias and discrimination. Standards of fairness can be complex and difficult to define be- cause perceptions of fairness differ among cultures and may shift depending on application. Organiza...
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
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AI Risks and Trustworthiness > Fair – with Harmful Bias Managed
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/framework-ai-rmf-1.0
Framework (AI RMF 1.0) January 2023 Gina M. Raimondo, Secretary 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. Su...
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
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section
null
Framework (AI RMF 1.0)
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/framing-risk
Framing Risk AI risk management offers a path to minimize potential negative impacts of AI systems, such as threats to civil liberties and rights, while also providing opportunities to maximize positive impacts. Addressing, documenting, and managing AI risks and potential negative impacts effectively can lead to more ...
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section
null
Framing Risk
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/govern
Govern The GOVERN function: • cultivates and implements a culture of risk management within organizations design- ing, developing, deploying, evaluating, or acquiring AI systems; • outlines processes, documents, and organizational schemes that anticipate, identify, and manage the risks a system can pose, including to ...
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AI RMF Core > Govern
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/how-ai-risks-differ-from-traditional-software-risks
How AI Risks Differ from Traditional Software Risks As with traditional software, risks from AI-based technology can be bigger than an en- terprise, span organizations, and lead to societal impacts. AI systems also bring a set of risks that are not comprehensively addressed by current risk frameworks and approaches. S...
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Appendix B: > How AI Risks Differ from Traditional Software Risks
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/need-to-be-clearly-defined-and-differentiated.-human-ai-configurations-can-span
1. Human roles and responsibilities in decision making and overseeing AI systems need to be clearly defined and differentiated. Human-AI configurations can span from fully autonomous to fully manual. AI systems can autonomously make deci- sions, defer decision making to a human expert, or be used by a human decision ...
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Appendix C: > AI Risk Management and Human-AI Interaction > need to be clearly defined and differentiated. Human-AI configurations can span
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/organizational-integration-and-management-of-risk
Organizational Integration and Management of Risk AI risks should not be considered in isolation. Different AI actors have different responsi- bilities and awareness depending on their roles in the lifecycle. For example, organizations developing an AI system often will not have information about how the system may be...
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Framing Risk > Organizational Integration and Management of Risk
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/privacy-enhanced
Privacy-Enhanced Privacy refers generally to the norms and practices that help to safeguard human autonomy, identity, and dignity. These norms and practices typically address freedom from intrusion, limiting observation, or individuals' agency to consent to disclosure or control of facets of their identities (e.g., bo...
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AI Risks and Trustworthiness > Privacy-Enhanced
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/risk-measurement
Challenges for AI Risk Management Several challenges are described below. They should be taken into account when managing risks in pursuit of AI trustworthiness. Risk Measurement AI risks or failures that are not well-defined or adequately understood are difficult to mea- sure quantitatively or qualitatively. The in...
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Framing Risk > Risk Measurement
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/risk-prioritization
Risk Prioritization Attempting to eliminate negative risk entirely can be counterproductive in practice because not all incidents and failures can be eliminated. Unrealistic expectations about risk may lead organizations to allocate resources in a manner that makes risk triage inefficient or impractical or wastes scar...
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Framing Risk > Risk Prioritization
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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AI-100-1/section/risk-tolerance
Risk Tolerance While the AI RMF can be used to prioritize risk, it does not prescribe risk tolerance. Risk tolerance refers to the organization's or AI actor's (see Appendix A) readiness to bear the risk in order to achieve its objectives. Risk tolerance can be influenced by legal or regula- tory requirements (Adapted...
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
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section
null
Framing Risk > Risk Tolerance
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/safe
Safe AI systems should "not under defined conditions, lead to a state in which human life, health, property, or the environment is endangered" (Source: ISO/IEC TS 5723:2022). Safe operation of AI systems is improved through: • responsible design, development, and deployment practices; • clear information to deployers ...
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AI Risks and Trustworthiness > Safe
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/secure-and-resilient
Secure and Resilient AI systems, as well as the ecosystems in which they are deployed, may be said to be re- silient if they can withstand unexpected adverse events or unexpected changes in their envi- ronment or use - or if they can maintain their functions and structure in the face of internal and external change an...
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AI Risks and Trustworthiness > Secure and Resilient
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/understanding-and-addressing-risks-impacts-and-harms
Understanding and Addressing Risks, Impacts, and Harms In the context of the AI RMF, risk refers to the composite measure of an event's probability of occurring and the magnitude or degree of the consequences of the corresponding event. The impacts, or consequences, of AI systems can be positive, negative, or both and...
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Framing Risk > Understanding and Addressing Risks, Impacts, and Harms
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-100-1/section/valid-and-reliable
Valid and Reliable Validation is the "confirmation, through the provision of objective evidence, that the re- quirements for a specific intended use or application have been fulfilled" (Source: ISO 9000:2015). Deployment of AI systems which are inaccurate, unreliable, or poorly gener- alized to data and settings beyon...
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AI Risks and Trustworthiness > Valid and Reliable
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1
AI-RMF-PLAYBOOK/ai_rmf_subcategory/govern-1.1
GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented. About: AI systems may be subject to specific applicable legal and regulatory requirements. Some legal requirements can mandate (e.g., nondiscrimination, data privacy and security controls) documentation, disclosure, and...
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AI RMF Playbook > Govern > GOVERN 1.1
https://airc.nist.gov/docs/playbook.json
aecbee3d3c8820816d295b11d10fb61324b17c25c2ff39ee95e2aa5654555bba
AI-RMF-PLAYBOOK/ai_rmf_subcategory/govern-1.2
GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures. About: Policies, processes, and procedures are central components of effective AI risk management and fundamental to individual and organizational accountability. All stakeholders benefit from poli...
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NIST AI RMF Playbook
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AI RMF Playbook > Govern > GOVERN 1.2
https://airc.nist.gov/docs/playbook.json
aecbee3d3c8820816d295b11d10fb61324b17c25c2ff39ee95e2aa5654555bba
AI-RMF-PLAYBOOK/ai_rmf_subcategory/govern-1.3
GOVERN 1.3: Processes and procedures are in place to determine the needed level of risk management activities based on the organization's risk tolerance. About: Risk management resources are finite in any organization. Adequate AI governance policies delineate the mapping, measurement, and prioritization of risks to a...
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AI RMF Playbook > Govern > GOVERN 1.3
https://airc.nist.gov/docs/playbook.json
aecbee3d3c8820816d295b11d10fb61324b17c25c2ff39ee95e2aa5654555bba