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NIST AI RMF · Subcategory 4.2

Measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended. Results are documented.

Summary

Measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended. Results are documented.

accountability and transparencyexplainability and interpretabilityfairness and biasparticipationprivacysafetysecure and resilienttevvtrustworthy characteristicsvalidity and reliability

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

← Subcategory 4.2

Internal risk controls for components of the AI system including third-party AI technologies are identified and documented.

Subcategory 4.3 →

Organizational practices are in place to enable AI testing, identification of incidents, and information sharing.

Track NIST AI RMF subcategory 4.2 as evidence

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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.