NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 1.3

MEASURE 1.3: Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates. Domain experts, users, AI actors external to the team that developed or deployed the AI system, and affected communities are consulted in support of assessments as necessary per organizational risk tolerance.

About:
The current AI systems are brittle, the failure modes are not well described, and the systems are dependent on the context in which they were developed and do not transfer well outside of the training environment. A reliance on local evaluations will be necessary along with a continuous monitoring of these systems. Measurements that extend beyond classical measures (which average across test cases) or expand to focus on pockets of failures where there are potentially significant costs can improve the reliability of risk management activities. Feedback from affected communities about how AI systems are being used can make AI evaluation purposeful. Involving internal experts who did not serve as front-line developers for the system and/or independent assessors regular assessments of AI systems helps a fulsome characterization of AI systems' performance and trustworthiness .

Suggested Actions:
- Evaluate TEVV processes regarding incentives to identify risks and impacts.
- Utilize separate testing teams established in the Govern function (2.1 and 4.1) to enable independent decisions and course-correction for AI systems. Track processes and measure and document change in performance.
- Plan and evaluate AI system prototypes with end user populations early and continuously in the AI lifecycle. Document test outcomes and course correct.
- Assess independence and stature of TEVV and oversight AI actors, to ensure they have the required levels of independence and resources to perform assurance, compliance, and feedback tasks effectively
- Evaluate interdisciplinary and demographically diverse internal team established in Map 1.2
- Evaluate effectiveness of external stakeholder feedback mechanisms, specifically related to processes for eliciting, evaluating and integrating input from diverse groups.
- Evaluate effectiveness of external stakeholder feedback mechanisms for enhancing AI actor visibility and decision making regarding AI system risks and trustworthy characteristics.
- Identify and utilize participatory approaches for assessing impacts that may arise from changes in system deployment (e.g., introducing new technology, decommissioning algorithms and models, adapting system, model or algorithm)

Transparency and Documentation:
### Organizations can document the following

- What are the roles, responsibilities, and delegation of authorities of personnel involved in the design, development, deployment, assessment and monitoring of the AI system?
- How easily accessible and current is the information available to external stakeholders?
- To what extent does the entity communicate its AI strategic goals and objectives to the community of stakeholders?
- To what extent can users or parties affected by the outputs of the AI system test the AI system and provide feedback?
- To what extent is this information sufficient and appropriate to promote transparency? Do external stakeholders have access to information on the design, operation, and limitations of the AI system?
- What type of information is accessible on the design, operations, and limitations of the AI system to external stakeholders, including end users, consumers, regulators, and individuals impacted by use of the AI system?

### AI Transparency Resources

- GAO-21-519SP - Artificial Intelligence: An Accountability Framework for Federal Agencies & Other Entities. [URL](https://www.gao.gov/products/gao-21-519sp)
- Artificial Intelligence Ethics Framework For The Intelligence Community. [URL](https://www.intelligence.gov/artificial-intelligence-ethics-framework-for-the-intelligence-community)

References:
Board of Governors of the Federal Reserve System. "SR 11-7: Guidance on Model Risk Management." April 4, 2011. [URL](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm)

"Definition of independent verification and validation (IV&V)", in IEEE 1012, IEEE Standard for System, Software, and Hardware Verification and Validation. Annex C, [URL](https://people.eecs.ku.edu/~hossein/Teaching/Stds/1012.pdf)

Mona Sloane, Emanuel Moss, Olaitan Awomolo, and Laura Forlano. "Participation Is Not a Design Fix for Machine Learning." Equity and Access in Algorithms, Mechanisms, and Optimization, October 2022. [URL](https://doi.org/10.1145/3551624.3555285)

Rediet Abebe and Kira Goldner. "Mechanism Design for Social Good." AI Matters 4, no. 3 (October 2018): 27-34. [URL](https://doi.org/10.1145/3284751.3284761)

Upol Ehsan, Ranjit Singh, Jacob Metcalf and Mark O. Riedl. "The Algorithmic Imprint." Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (2022). [URL] (https://arxiv.org/pdf/2206.03275v1)