CLAIMBOUND / SOURCE-PUBLICATION EVIDENCE
AI safety
Every card identifies what it proves, what it does not prove, its exact quote, locator, source hash, and local rerun command.
1. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 6
Narrow public claim
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 environment and our planet.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 6- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
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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 environment and our planet.
2. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 6
Narrow public claim
AI technologies can drive inclusive economic growth and support scientific advancements that improve the conditions of our world.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 6- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
AI technologies can drive inclusive economic growth and support scientific advancements that improve the conditions of our world.
3. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 7
Narrow public claim
Social responsibility can refer to the organization’s responsibility “for the impacts of its decisions and activities on society and the environment through transparent and ethical behavior” (ISO 26000:2010).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 7- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Social responsibility can refer to the organization’s responsibility “for the impacts of its decisions and activities on society and the environment through transparent and ethical behavior” (ISO 26000:2010).
4. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 7
Narrow public claim
Sustainability refers to the “state of the global system, including environmental, social, and economic aspects, in which the needs of the present are met without compromising the ability of future generations to meet their own needs” (ISO/IEC TR 24368:2022).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 7- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Sustainability refers to the “state of the global system, including environmental, social, and economic aspects, in which the needs of the present are met without compromising the ability of future generations to meet their own needs” (ISO/IEC TR 24368:2022).
5. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 8
Narrow public claim
Development of the AI RMF by NIST in collaboration with the private and public sectors is directed and consistent with its broader AI efforts called for by the National AI Initiative Act of 2020, the National Security Commission on Artificial Intelligence recommendations, and the Plan for Federal Engagement in Developing Technical Standards and Related Tools.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 8- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Development of the AI RMF by NIST in collaboration with the private and public sectors is directed and consistent with its broader AI efforts called for by the National AI Initiative Act of 2020, the National Security Commission on Artificial Intelligence recommendations, and the Plan for Federal Engagement in Developing Technical Standards and Related Tools.
6. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 8
Narrow public claim
Priority research and additional guidance that will enhance this Framework will be captured in an associated AI Risk Management Framework Roadmap to which NIST and thebroadercommunitycancontribute.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 8- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Priority research and additional guidance that will enhance this Framework will be captured in an associated AI Risk Management Framework Roadmap to which NIST and thebroadercommunitycancontribute.
7. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 9
Narrow public claim
The impacts, or consequences, of AI systems can be positive, negative, or both and can result in opportunities or threats (Adapted from: ISO 31000:2018).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 9- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
The impacts, or consequences, of AI systems can be positive, negative, or both and can result in opportunities or threats (Adapted from: ISO 31000:2018).
8. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 9
Narrow public claim
When considering the negative impact of a potential event, risk is a function of 1) the negative impact, or magnitude of harm, that would arise if the circumstance or event occurs and 2) the likelihood of occurrence (Adapted from: OMB Circular A-130:2016).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 9- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
When considering the negative impact of a potential event, risk is a function of 1) the negative impact, or magnitude of harm, that would arise if the circumstance or event occurs and 2) the likelihood of occurrence (Adapted from: OMB Circular A-130:2016).
9. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 10
Narrow public claim
AI risks or failures that are not well-defined or adequately understood are difficult to measurequantitativelyorqualitatively.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 10- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
AI risks or failures that are not well-defined or adequately understood are difficult to measurequantitativelyorqualitatively.
10. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 10
Narrow public claim
Regardless,allpartiesandAIactorsshould manage risk in the AI systems they develop, deploy, or use as standalone or integrated components.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 10- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t01_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Regardless,allpartiesandAIactorsshould manage risk in the AI systems they develop, deploy, or use as standalone or integrated components.
11. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 11
Narrow public claim
Availability of reliable metrics: The current lack of consensus on robust and verifiable measurement methods for risk and trustworthiness, and applicability to different AI use cases, is an AI risk measurement challenge.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 11- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Availability of reliable metrics: The current lack of consensus on robust and verifiable measurement methods for risk and trustworthiness, and applicability to different AI use cases, is an AI risk measurement challenge.
12. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 11
Narrow public claim
In addition, measurement approaches can be oversimplified, gamed, lack critical nuance, become relied upon in unexpected ways, or fail to account for differences in affected groups andcontexts.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 11- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
In addition, measurement approaches can be oversimplified, gamed, lack critical nuance, become relied upon in unexpected ways, or fail to account for differences in affected groups andcontexts.
13. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 12
Narrow public claim
Risk tolerance can be influenced by legal or regulatory requirements (Adapted from: ISO GUIDE 73).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 12- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Risk tolerance can be influenced by legal or regulatory requirements (Adapted from: ISO GUIDE 73).
14. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 12
Narrow public claim
Risk tolerances can be influenced by policies and norms established by AI system owners, organizations, industries, communities, or policy makers.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 12- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Risk tolerances can be influenced by policies and norms established by AI system owners, organizations, industries, communities, or policy makers.
15. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 13
Narrow public claim
When applying the AI RMF, risks which the organization determines to be highest for the AI systems within a given context of use call for the most urgent prioritization and most thorough risk management process.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 13- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
When applying the AI RMF, risks which the organization determines to be highest for the AI systems within a given context of use call for the most urgent prioritization and most thorough risk management process.
16. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 13
Narrow public claim
The AI RMF may be utilized along with related guidance and frameworks for managing AI system risks or broader enterprise risks.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 13- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
The AI RMF may be utilized along with related guidance and frameworks for managing AI system risks or broader enterprise risks.
17. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 14
Narrow public claim
Organizations need to establish and maintain the appropriate accountability mechanisms, roles and responsibilities, culture, and incentive structures for risk management to be effective.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 14- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Organizations need to establish and maintain the appropriate accountability mechanisms, roles and responsibilities, culture, and incentive structures for risk management to be effective.
18. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 14
Narrow public claim
The People & Planet dimension at the center of Figure 2 represents human rights and the broader well-being of society and the planet.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 14- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
The People & Planet dimension at the center of Figure 2 represents human rights and the broader well-being of society and the planet.
19. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 17
Narrow public claim
For AI systems to be trustworthy, they often need to be responsive to a multiplicity of criteria that are of value to interested parties.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 17- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
For AI systems to be trustworthy, they often need to be responsive to a multiplicity of criteria that are of value to interested parties.
20. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 17
Narrow public claim
Characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 17- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t02_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.
21. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 18
Narrow public claim
For example, subject matter experts can assist in the evaluation of TEVV findings and work with product and deployment teams to align TEVV parameters to requirements and deployment conditions.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 18- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t03_primary_claims.json --source-file <local-raw-pdf>Exact source quote
For example, subject matter experts can assist in the evaluation of TEVV findings and work with product and deployment teams to align TEVV parameters to requirements and deployment conditions.
22. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 18
Narrow public claim
Validation is the “confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled” (Source: ISO 9000:2015).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 18- Captured
- 2026-08-04
- Source SHA-256
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Validation is the “confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled” (Source: ISO 9000:2015).
23. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 19
Narrow public claim
Accuracy is defined by ISO/IEC TS 5723:2022 as “closeness of results of observations, computations, or estimates to the true values or the values accepted as being true.” Measures of accuracy should consider computational-centric measures (e.g., false positive and false negative rates), human-AI teaming, and demonstrate external validity (generalizable beyond the training conditions).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
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- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 19- Captured
- 2026-08-04
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Accuracy is defined by ISO/IEC TS 5723:2022 as “closeness of results of observations, computations, or estimates to the true values or the values accepted as being true.” Measures of accuracy should consider computational-centric measures (e.g., false positive and false negative rates), human-AI teaming, and demonstrate external validity (generalizable beyond the training conditions).
24. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 19
Narrow public claim
Robustness or generalizability is defined as the “ability of a system to maintain its level of performance under a variety of circumstances” (Source: ISO/IEC TS 5723:2022).
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 19- Captured
- 2026-08-04
- Source SHA-256
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Robustness or generalizability is defined as the “ability of a system to maintain its level of performance under a variety of circumstances” (Source: ISO/IEC TS 5723:2022).
25. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 20
Narrow public claim
AI safety risk management approaches should take cues from efforts and guidelines for safety in fields such as transportation and healthcare, and align with existing sector- or application-specificguidelinesorstandards.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 20- Captured
- 2026-08-04
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AI safety risk management approaches should take cues from efforts and guidelines for safety in fields such as transportation and healthcare, and align with existing sector- or application-specificguidelinesorstandards.
26. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 20
Narrow public claim
AI systems that can maintain confidentiality, integrity, and availability through protection mechanisms that prevent unauthorized access and use may be said to be secure.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 20- Captured
- 2026-08-04
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AI systems that can maintain confidentiality, integrity, and availability through protection mechanisms that prevent unauthorized access and use may be said to be secure.
27. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 21
Narrow public claim
However, it is difficult to determine whether an opaque system possesses such characteristics, and to do so over time as complex systems evolve.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 21- Captured
- 2026-08-04
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However, it is difficult to determine whether an opaque system possesses such characteristics, and to do so over time as complex systems evolve.
28. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 21
Narrow public claim
When consequences are severe, such as when life and liberty are at stake, AI developers and deployers should consider proportionally and proactively adjusting their transparency andaccountabilitypractices.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 21- Captured
- 2026-08-04
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When consequences are severe, such as when life and liberty are at stake, AI developers and deployers should consider proportionally and proactively adjusting their transparency andaccountabilitypractices.
29. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 22
Narrow public claim
Risks to interpretability often can be addressed by communicating a description of why an AI system made a particular prediction or recommendation.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 22- Captured
- 2026-08-04
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Risks to interpretability often can be addressed by communicating a description of why an AI system made a particular prediction or recommendation.
30. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 22
Narrow public claim
Privacy-related risks may influence security, bias, and transparency and come with tradeoffs with these other characteristics.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 22- Captured
- 2026-08-04
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Privacy-related risks may influence security, bias, and transparency and come with tradeoffs with these other characteristics.
31. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 23
Narrow public claim
NISThasidentified three major categories of AI bias to be considered and managed: systemic, computational and statistical, and human-cognitive.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 23- Captured
- 2026-08-04
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NISThasidentified three major categories of AI bias to be considered and managed: systemic, computational and statistical, and human-cognitive.
32. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 23
Narrow public claim
Systemic bias can be present in AI datasets, the organizational norms, practices, and processes across the AI lifecycle, and the broader society that uses AI systems.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 23- Captured
- 2026-08-04
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Systemic bias can be present in AI datasets, the organizational norms, practices, and processes across the AI lifecycle, and the broader society that uses AI systems.
33. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 24
Narrow public claim
Evaluations of AI RMF effectiveness – including ways to measure bottom-line improvements in the trustworthiness of AI systems – will be part of future NIST activities, in conjunctionwiththeAIcommunity.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 24- Captured
- 2026-08-04
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Evaluations of AI RMF effectiveness – including ways to measure bottom-line improvements in the trustworthiness of AI systems – will be part of future NIST activities, in conjunctionwiththeAIcommunity.
34. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 26
Narrow public claim
An online companion resource to the AI RMF, the NIST AI RMF Playbook, is available to help organizations navigate the AI RMF and achieve its outcomes through suggested tactical actions they can apply within their own contexts.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 26- Captured
- 2026-08-04
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An online companion resource to the AI RMF, the NIST AI RMF Playbook, is available to help organizations navigate the AI RMF and achieve its outcomes through suggested tactical actions they can apply within their own contexts.
35. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 26
Narrow public claim
Like the AI RMF, the Playbook is voluntary and organizations can utilize the suggestions according to their needs and interests.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 26- Captured
- 2026-08-04
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Like the AI RMF, the Playbook is voluntary and organizations can utilize the suggestions according to their needs and interests.
36. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 27
Narrow public claim
Aspectsof GOVERN,especiallythoserelatedto compliance or evaluation, should be integrated into each of the other functions.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 27- Captured
- 2026-08-04
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Aspectsof GOVERN,especiallythoserelatedto compliance or evaluation, should be integrated into each of the other functions.
37. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 27
Narrow public claim
Governing authorities can determine the overarching policies that direct an organization’s mission, goals, values, culture, and risk tolerance.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 27- Captured
- 2026-08-04
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Governing authorities can determine the overarching policies that direct an organization’s mission, goals, values, culture, and risk tolerance.
38. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 28
Narrow public claim
(Continued) Categories Subcategories 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 determiningthefrequencyofperiodicreview.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 28- Captured
- 2026-08-04
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(Continued) Categories Subcategories 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 determiningthefrequencyofperiodicreview.
39. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 28
Narrow public claim
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.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 28- Captured
- 2026-08-04
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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.
40. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 29
Narrow public claim
GOVERN 5: GOVERN 5.1: Organizational policies and practices are in place Processesarein to collect, consider, prioritize, and integrate feedback from those placeforrobust external to the team that developed or deployed the AI system engagementwith regarding the potential individual and societal impacts related to relevantAIactors.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 29- Captured
- 2026-08-04
- Source SHA-256
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python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t04_primary_claims.json --source-file <local-raw-pdf>Exact source quote
GOVERN 5: GOVERN 5.1: Organizational policies and practices are in place Processesarein to collect, consider, prioritize, and integrate feedback from those placeforrobust external to the team that developed or deployed the AI system engagementwith regarding the potential individual and societal impacts related to relevantAIactors.
41. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 29
Narrow public claim
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 andimplementation.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 29- Captured
- 2026-08-04
- Source SHA-256
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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 andimplementation.
42. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 30
Narrow public claim
The information gathered while carrying out the MAP function enables negative risk prevention and informs decisions for processes such as model management, as well as an initial decision about appropriateness or the need for an AI solution.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 30- Captured
- 2026-08-04
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The information gathered while carrying out the MAP function enables negative risk prevention and informs decisions for processes such as model management, as well as an initial decision about appropriateness or the need for an AI solution.
43. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 30
Narrow public claim
Without contextual knowledge, and awareness of risks within the identified contexts, risk management is difficult to perform.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 30- Captured
- 2026-08-04
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Without contextual knowledge, and awareness of risks within the identified contexts, risk management is difficult to perform.
44. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 31
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MAP 2.2: Information about the AI system’s knowledge limits and how system output may be utilized and overseen by humans is documented.
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MAP 2.2: Information about the AI system’s knowledge limits and how system output may be utilized and overseen by humans is documented.
45. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 31
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Documentation provides sufficient information to assist relevant AI actors when making decisions and taking subsequentactions.
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Documentation provides sufficient information to assist relevant AI actors when making decisions and taking subsequentactions.
46. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 32
Narrow public claim
MAP 5: Impactsto MAP 5.1: Likelihood and magnitude of each identified impact individuals,groups, (both potentially beneficial and harmful) based on expected use, communities, past uses of AI systems in similar contexts, public incident reorganizations,and ports, feedback from those external to the team that developed societyare or deployed the AI system, or other data are identified and characterized. documented.
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MAP 5: Impactsto MAP 5.1: Likelihood and magnitude of each identified impact individuals,groups, (both potentially beneficial and harmful) based on expected use, communities, past uses of AI systems in similar contexts, public incident reorganizations,and ports, feedback from those external to the team that developed societyare or deployed the AI system, or other data are identified and characterized. documented.
47. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 33
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The MEASURE function employs quantitative, qualitative, or mixed-method tools, techniques, and methodologies to analyze, assess, benchmark, and monitor AI risk and related impacts.
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- Section locator
PDF page 33- Captured
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The MEASURE function employs quantitative, qualitative, or mixed-method tools, techniques, and methodologies to analyze, assess, benchmark, and monitor AI risk and related impacts.
48. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 33
Narrow public claim
Processes developed or adopted in the MEASURE function should include rigorous software testing and performance assessment methodologies with associated measures of uncertainty, comparisons to performance benchmarks, and formalizedreportinganddocumentationofresults.
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Processes developed or adopted in the MEASURE function should include rigorous software testing and performance assessment methodologies with associated measures of uncertainty, comparisons to performance benchmarks, and formalizedreportinganddocumentationofresults.
49. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 34
Narrow public claim
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.
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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.
50. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 34
Narrow public claim
MEASURE 2: AI MEASURE2.1: Testsets,metrics,anddetailsaboutthetoolsused systemsare duringTEVVaredocumented. evaluatedfor MEASURE 2.2: Evaluations involving human subjects meet aptrustworthy plicable requirements (including human subject protection) and characteristics. arerepresentativeoftherelevantpopulation.
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MEASURE 2: AI MEASURE2.1: Testsets,metrics,anddetailsaboutthetoolsused systemsare duringTEVVaredocumented. evaluatedfor MEASURE 2.2: Evaluations involving human subjects meet aptrustworthy plicable requirements (including human subject protection) and characteristics. arerepresentativeoftherelevantpopulation.
51. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 35
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(Continued) Categories Subcategories MEASURE 2.6: The AI system is evaluated regularly for safety risks–asidentifiedintheMAPfunction.
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(Continued) Categories Subcategories MEASURE 2.6: The AI system is evaluated regularly for safety risks–asidentifiedintheMAPfunction.
52. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 35
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MEASURE 2.8: Risks associatedwith transparency andaccountability – as identified in the MAP function – are examined and documented.
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MEASURE 2.8: Risks associatedwith transparency andaccountability – as identified in the MAP function – are examined and documented.
53. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 36
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MEASURE 4: MEASURE4.1: MeasurementapproachesforidentifyingAIrisks Feedbackabout are connected to deployment context(s) and informed through efficacyof consultation with domain experts and other end users.
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MEASURE 4: MEASURE4.1: MeasurementapproachesforidentifyingAIrisks Feedbackabout are connected to deployment context(s) and informed through efficacyof consultation with domain experts and other end users.
54. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 36
Narrow public claim
Apmeasurementis proachesaredocumented. gatheredand MEASURE 4.2: Measurement results regarding AI system trustassessed. worthiness 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.
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The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
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PDF page 36- Captured
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Apmeasurementis proachesaredocumented. gatheredand MEASURE 4.2: Measurement results regarding AI system trustassessed. worthiness 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.
55. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 37
Narrow public claim
MANAGE 3: AI MANAGE 3.1: AI risks and benefits from third-party resources risksandbenefits are regularly monitored, and risk controls are applied and fromthird-party documented. entitiesare MANAGE 3.2: Pre-trained models which are used for developmanaged. ment are monitored as part of AI system regular monitoring and maintenance.
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The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
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- Section locator
PDF page 37- Captured
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MANAGE 3: AI MANAGE 3.1: AI risks and benefits from third-party resources risksandbenefits are regularly monitored, and risk controls are applied and fromthird-party documented. entitiesare MANAGE 3.2: Pre-trained models which are used for developmanaged. ment are monitored as part of AI system regular monitoring and maintenance.
56. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 38
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Profiles may illustrate and offer insights into how risk can be managed at various stages of the AI lifecycle or in specific sector, technology, or end-use applications.
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Profiles may illustrate and offer insights into how risk can be managed at various stages of the AI lifecycle or in specific sector, technology, or end-use applications.
57. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 38
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An AI RMF Current Profile indicates how AI is currently being managed and the related risks in terms of current outcomes.
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An AI RMF Current Profile indicates how AI is currently being managed and the related risks in terms of current outcomes.
58. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 40
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Appendix A: 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.
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Appendix A: 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.
59. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 40
Narrow public claim
AI Deployment actors are responsible for contextual decisions relating to how the AI system is used to assure deployment of the system into production.
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AI Deployment actors are responsible for contextual decisions relating to how the AI system is used to assure deployment of the system into production.
60. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 41
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Domain Expert tasks involve input from multidisciplinary practitioners or scholars who provide knowledge or expertise in – and about – an industry sector, economic sector, context, or application area where an AI system is being used.
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Domain Expert tasks involve input from multidisciplinary practitioners or scholars who provide knowledge or expertise in – and about – an industry sector, economic sector, context, or application area where an AI system is being used.
61. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 42
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AdditionalAIActors Third-party entities include providers, developers, vendors, and evaluators of data, algorithms, models, and/or systems and related services for another organization or the organization’s customers or clients.
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AdditionalAIActors Third-party entities include providers, developers, vendors, and evaluators of data, algorithms, models, and/or systems and related services for another organization or the organization’s customers or clients.
62. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 42
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They can include trade associations, standards developing organizations, advocacy groups, researchers, environmental groups, and civil society organizations.
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They can include trade associations, standards developing organizations, advocacy groups, researchers, environmental groups, and civil society organizations.
63. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 43
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Forexample,pre-trained models and transfer learning can advance research and increase accuracy and resilience whencomparedtoothermodelsandapproaches.
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Forexample,pre-trained models and transfer learning can advance research and increase accuracy and resilience whencomparedtoothermodelsandapproaches.
64. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 43
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Identifyingcontextualfactorsinthe MAP function will assist AI actors in determining the level of risk and potential management efforts.
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The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
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python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Identifyingcontextualfactorsinthe MAP function will assist AI actors in determining the level of risk and potential management efforts.
65. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 44
Narrow public claim
Privacy and cybersecurity risk management considerations and approaches are applicable in the design, development, deployment, evaluation, and use of AI systems.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 44- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Privacy and cybersecurity risk management considerations and approaches are applicable in the design, development, deployment, evaluation, and use of AI systems.
66. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 44
Narrow public claim
Privacy and cybersecurity risks are also considered as part of broader enterprise risk management considerations,whichmayincorporateAIrisks.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 44- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
Privacy and cybersecurity risks are also considered as part of broader enterprise risk management considerations,whichmayincorporateAIrisks.
67. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 45
Narrow public claim
The AI RMF provides opportunities to clearly define and differentiate the various human roles and responsibilities when using, interacting with, or managing AI systems.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 45- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
The AI RMF provides opportunities to clearly define and differentiate the various human roles and responsibilities when using, interacting with, or managing AI systems.
68. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 45
Narrow public claim
AI systems can autonomously make decisions, defer decision making to a human expert, or be used by a human decision maker as an additional opinion.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 45- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
AI systems can autonomously make decisions, defer decision making to a human expert, or be used by a human decision maker as an additional opinion.
69. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 46
Narrow public claim
The GOVERN function provides organizations with the opportunity to clarify and define the roles and responsibilities for the humans in the Human-AI team configurations and those who are overseeing the AI system performance.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 46- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
The GOVERN function provides organizations with the opportunity to clarify and define the roles and responsibilities for the humans in the Human-AI team configurations and those who are overseeing the AI system performance.
70. NIST Artificial Intelligence Risk Management Framework 1.0 · PDF page 47
Narrow public claim
The AI RMF should allow for communication of AI risks across an organization, between organizations, with customers, and to the public at large.
What this card proves
The exact quoted statement occurred in the named official PDF fetched on 2026-08-04, and the complete PDF matched the recorded SHA-256.
What this card does not prove
It does not independently establish the underlying model capability, safety, comparison, or real-world truth. This is a retrospective single-maintainer source-publication audit.
- Primary source
- NIST Artificial Intelligence Risk Management Framework 1.0
- Section locator
PDF page 47- Captured
- 2026-08-04
- Source SHA-256
7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1- Reproduction
- Maintainer run only; no independent operator rerun is registered.
Repeat this exact check locally
Download the PDF to a local file, then run the committed verifier. A changed PDF is reported as drift rather than silently accepted.
python scripts/verify_primary_public_claim_group.py artifacts/cb7k_dom003_t07_primary_claims.json --source-file <local-raw-pdf>Exact source quote
The AI RMF should allow for communication of AI risks across an organization, between organizations, with customers, and to the public at large.