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Safety cases, assurance plans, and documented evidence of safety claims.
Also in Risk & Assurance
Reasoning
Measures system performance qualitatively and quantitatively in deployment-similar conditions; testing and evaluation activity.
Consider baseline model performance on suites of benchmarks when selecting a model for fine tuning or enhancement with retrieval-augmented generation.
2.2.2 Testing & EvaluationEvaluate claims of model capabilities using empirically validated methods
2.2.2 Testing & EvaluationShare results of pre-deployment testing with relevant GAI Actors, such as those with system release approval authority
2.2.2 Testing & EvaluationUtilize a purpose-built testing environment such as NIST Dioptra to empirically evaluate GAI trustworthy characteristics.
2.2.2 Testing & EvaluationLegal and regulatory requirements involving AI are understood, managed, and documented.
2.1.3 Policies & ProceduresLegal and regulatory requirements involving AI are understood, managed, and documented. > Align GAI development and use with applicable laws and regulations, including those related to data privacy, copyright and intellectual property law.
2.1.3 Policies & ProceduresThe characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.
2.1.3 Policies & ProceduresThe characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. > Establish transparency policies and processes for documenting the origin and history of training data and generated data for GAI applications to advance digital content transparency, while balancing the proprietary nature of training approaches.
2.1.3 Policies & ProceduresThe characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. > Establish policies to evaluate risk-relevant capabilities of GAI and robustness of safety measures, both prior to deployment and on an ongoing basis, through internal and external evaluations.
2.1.3 Policies & ProceduresProcesses, procedures, and practices are in place to determine the needed level of risk management activities based on the organization’s risk tolerance.
2.1.3 Policies & ProceduresArtificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1)
US National Institute of Standards and Technology (NIST) (2024)
This document is a cross-sectoral profile of and companion resource for the AI Risk Management Framework (AI RMF 1.0) for Generative AI, 1 pursuant to President Biden’s Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence.2 The AI RMF was released in January 2023, and is intended for voluntary use and to improve the ability of organizations to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.
Verify and Validate
Testing, evaluating, auditing, and red-teaming the AI system
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
