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NIST AI RMF · Subcategory 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 – and to inform responsible use and governance.

Summary

The AI model is explained, validated, and documented, and AI system output is interpreted within its context – as identified in the MAP function – and to inform responsible use and governance.

explainability and interpretabilitytevvtrustworthy characteristics

Read the full official text: https://airc.nist.gov/airmf-resources/playbook/

← Subcategory 2.8

Risks associated with transparency and accountability – as identified in the MAP function – are examined and documented.

Subcategory 2.10 →

Privacy risk of the AI system – as identified in the MAP function – is examined and documented.

Track NIST AI RMF subcategory 2.9 as evidence

eurocompliant maps this obligation to a checklist task and the evidence that satisfies it, alongside every other framework you follow.

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More from NIST AI RMF

Subcategory 1.1

Legal and regulatory requirements involving AI are understood, managed, and documented.

Subcategory 1.1

A determination is made as to whether the AI system achieves its intended purpose and stated objectives and whether its development or deployment should proceed.

Subcategory 1.1

Intended purpose, 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: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics.

Subcategory 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.