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Governance frameworks, formal policies, and strategic alignment mechanisms.
Also in Oversight & Accountability
Reasoning
Testing and evaluation involving human subjects to assess system performance and safety across representative populations.
Assess and manage statistical biases related to GAI content provenance through techniques such as re-sampling, re-weighting, or adversarial training.
1.1.2 Learning ObjectivesDocument how content provenance data is tracked and how that data interacts with privacy and security. Consider: Anonymizing data to protect the privacy of human subjects; Leveraging privacy output filters; Removing any personally identifiable information (PII) to prevent potential harm or misuse.
2.2.4 Assurance DocumentationProvide human subjects with options to withdraw participation or revoke their consent for present or future use of their data in GAI applications.
2.1.3 Policies & ProceduresUse techniques such as anonymization, differential privacy or other privacyenhancing technologies to minimize the risks associated with linking AI-generated content back to individual human subjects.
1.2.4 Security InfrastructureLegal 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
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Measure
Quantifying, testing, and monitoring identified AI risks
