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Safety cases, assurance plans, and documented evidence of safety claims.
Also in Risk & Assurance
Reasoning
Examines and documents risks associated with transparency and accountability through structured analysis and prioritization.
Compile statistics on actual policy violations, take-down requests, and intellectual property infringement for organizational GAI systems: Analyze transparency reports across demographic groups, languages groups.
2.3.3 Monitoring & LoggingDocument the instructions given to data annotators or AI red-teamers.
2.2.4 Assurance DocumentationUse digital content transparency solutions to enable the documentation of each instance where content is generated, modified, or shared to provide a tamperproof history of the content, promote transparency, and enable traceability. Robust version control systems can also be applied to track changes across the AI lifecycle over time.
1.2.5 Provenance & WatermarkingVerify adequacy of GAI system user instructions through user testing.
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.
Plan and Design
Designing the AI system, defining requirements, and planning development
Deployer
Entity that integrates and deploys the AI system for end users
Measure
Quantifying, testing, and monitoring identified AI risks
