GOVERN-1.2—The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures.

Policies, processes, and procedures are central components of effective AI risk management and fundamental to individual and organizational accountability. All stakeholders benefit from policies, processes, and procedures which require preventing harm by design and default.

Organizational policies and procedures will vary based on available resources and risk profiles, but can help systematize AI actor roles and responsibilities throughout the AI lifecycle. Without such policies, risk management can be subjective across the organization, and exacerbate rather than minimize risks over time. Policies, or summaries thereof, are understandable to relevant AI actors. Policies reflect an understanding of the underlying metrics, measurements, and tests that are necessary to support policy and AI system design, development, deployment and use.

Lack of clear information about responsibilities and chains of command will limit the effectiveness of risk management.

Organizations can document the following

• To what extent do these policies foster public trust and confidence in the use of the AI system?
• What policies has the entity developed to ensure the use of the AI system is consistent with its stated values and principles?
• What policies and documentation has the entity developed to encourage the use of its AI system as intended?
• To what extent are the model outputs consistent with the entity’s values and principles to foster public trust and equity?

AI Transparency Resources

GAO-21-519SP: AI Accountability Framework for Federal Agencies & Other Entities.

Off. Comptroller Currency, Comptroller’s Handbook: Model Risk Management (Aug. 2021).

GAO, “Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities,” GAO@100 (GAO-21-519SP), June 2021.

NIST, "U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools".

Lipton, Zachary and McAuley, Julian and Chouldechova, Alexandra, Does mitigating ML’s impact disparity require treatment disparity? Advances in Neural Information Processing Systems, 2018.

Jessica Newman (2023) “A Taxonomy of Trustworthiness for Artificial Intelligence: Connecting Properties of Trustworthiness with Risk Management and the AI Lifecycle,” UC Berkeley Center for Long-Term Cybersecurity.

Emily Hadley (2022). Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States. 2022 IEEE International Conference on Big Data (Big Data), 5029-5038.

SAS Institute, “The SAS® Data Governance Framework: A Blueprint for Success”.

ISO, “Information technology — Reference Model of Data Management, “ ISO/IEC TR 10032:200.

“Play 5: Create a formal policy,” Partnership on Employment & Accessible Technology (PEAT, peatworks.org).

"National Institute of Standards and Technology. (2018). Framework for improving critical infrastructure cybersecurity.

Kaitlin R. Boeckl and Naomi B. Lefkovitz. "NIST Privacy Framework: A Tool for Improving Privacy Through Enterprise Risk Management, Version 1.0." National Institute of Standards and Technology (NIST), January 16, 2020.

“plainlanguage.gov – Home,” The U.S. Government.