NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 1.2

MEASURE 1.2: Appropriateness of AI metrics and effectiveness of existing controls is regularly assessed and updated including reports of errors and impacts on affected communities.

About:
Different AI tasks, such as neural networks or natural language processing, benefit from different evaluation techniques. Use-case and particular settings in which the AI system is used also affects appropriateness of the evaluation techniques. Changes in the operational settings, data drift, model drift are among factors that suggest regularly assessing and updating appropriateness of AI metrics and their effectiveness can enhance reliability of AI system measurements.

Suggested Actions:
- Assess external validity of all measurements (e.g., the degree to which measurements taken in one context can generalize to other contexts).
- Assess effectiveness of existing metrics and controls on a regular basis throughout the AI system lifecycle.
- Document reports of errors, incidents and negative impacts and assess sufficiency and efficacy of existing metrics for repairs, and upgrades
- Develop new metrics when existing metrics are insufficient or ineffective for implementing repairs and upgrades.
- Develop and utilize metrics to monitor, characterize and track external inputs, including any third-party tools.
- Determine frequency and scope for sharing metrics and related information with stakeholders and impacted communities.
- Utilize stakeholder feedback processes established in the Map function to capture, act upon and share feedback from end users and potentially impacted communities.
- Collect and report software quality metrics such as rates of bug occurrence and severity, time to response, and time to repair (See Manage 4.3).

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

- What metrics has the entity developed to measure performance of the AI system?
- To what extent do the metrics provide accurate and useful measure of performance?
- What corrective actions has the entity taken to enhance the quality, accuracy, reliability, and representativeness of the data?
- How will the accuracy or appropriate performance metrics be assessed?
- What is the justification for the metrics selected?

### 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:
ACM Technology Policy Council. "Statement on Principles for Responsible Algorithmic Systems." Association for Computing Machinery (ACM), October 26, 2022. [URL](https://www.acm.org/binaries/content/assets/public-policy/final-joint-ai-statement-update.pdf)

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer-Verlag, 2009. [URL](https://hastie.su.domains/ElemStatLearn/)

Harini Suresh and John Guttag. "A Framework for Understanding Sources of Harm Throughout the Machine Learning Life Cycle." Equity and Access in Algorithms, Mechanisms, and Optimization, October 2021. [URL](https://doi.org/10.1145/3465416.3483305)

Christopher M. Bishop. Pattern Recognition and Machine Learning. New York: Springer, 2006. [URL](https://cis.temple.edu/~latecki/Courses/RobotFall08/BishopBook/Pages_from_PatternRecognitionAndMachineLearning1.pdf)

Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W. Duncan Wadsworth, and Hanna Wallach. "Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs." Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, July 2021, 368-78. [URL](https://doi.org/10.1145/3461702.3462610)