NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 4.1 (part 1)

MEASURE 4.1: Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented.

About:
AI Actors carrying out TEVV tasks may have difficulty evaluating impacts within the system context of use. AI system risks and impacts are often best described by end users and others who may be affected by output and subsequent decisions. AI Actors can elicit feedback from impacted individuals and communities via participatory engagement processes established in Govern 5.1 and 5.2, and carried out in Map 1.6, 5.1, and 5.2.

Activities described in the Measure function enable AI actors to evaluate feedback from impacted individuals and communities. To increase awareness of insights, feedback can be evaluated in close collaboration with AI actors responsible for impact assessment, human-factors, and governance and oversight tasks, as well as with other socio-technical domain experts and researchers. To gain broader expertise for interpreting evaluation outcomes, organizations may consider collaborating with advocacy groups and civil society organizations.

Insights based on this type of analysis can inform TEVV-based decisions about metrics and related courses of action.

Suggested Actions:
- Support mechanisms for capturing feedback from system end users (including domain experts, operators, and practitioners). Successful approaches are:
- conducted in settings where end users are able to openly share their doubts and insights about AI system output, and in connection to their specific context of use (including setting and task-specific lines of inquiry)
- developed and implemented by human-factors and socio-technical domain experts and researchers
- designed to ensure control of interviewer and end user subjectivity and biases
- Identify and document approaches
- for evaluating and integrating elicited feedback from system end users
- in collaboration with human-factors and socio-technical domain experts,
- to actively inform a process of continual improvement.
- Evaluate feedback from end users alongside evaluated feedback from impacted communities (MEASURE 3.3).
- Utilize end user feedback to investigate how selected metrics and measurement approaches interact with organizational and operational contexts.
- Analyze and document system-internal measurement processes in comparison to collected end user feedback.
- Identify and implement approaches to measure effectiveness and satisfaction with end user elicitation techniques, and document results.

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

- Did your organization address usability problems and test whether user interfaces served their intended purposes?
- How will user and peer engagement be integrated into the model development process and periodic performance review once deployed?
- 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 are the established procedures effective in mitigating bias, inequity, and other concerns resulting from the 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)
- WEF Companion to the Model AI Governance Framework - Implementation and Self-Assessment Guide for Organizations [URL](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/resource-for-organisation/ai/sgisago.ashx)

References:
Batya Friedman, and David G. Hendry. Value Sensitive Design: Shaping Technology with Moral Imagination. Cambridge, MA: The MIT Press, 2019. [URL](https://mitpress.mit.edu/9780262039536/value-sensitive-design/)

Batya Friedman, David G. Hendry, and Alan Borning. "A Survey of Value Sensitive Design Methods." Foundations and Trends in Human-Computer Interaction 11, no. 2 (November 22, 2017): 63-125. [URL](https://doi.org/10.1561/1100000015)

Steven Umbrello, and Ibo van de Poel. "Mapping Value Sensitive Design onto AI for Social Good Principles." AI and Ethics 1, no. 3 (February 1, 2021): 283-96. [URL](https://doi.org/10.1007/s43681-021-00038-3)

Karen Boyd. "Designing Up with Value-Sensitive Design: Building a Field Guide for Ethical ML Development." FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, June 20, 2022, 2069-82. [URL](https://doi.org/10.1145/3531146.3534626)

Janet Davis and Lisa P. Nathan. "Value Sensitive Design: Applications, Adaptations, and Critiques." In Handbook of Ethics, Values, and Technological Design, edited by Jeroen van den Hoven, Pieter E. Vermaas, and Ibo van de Poel, January 1, 2015, 11-40. [URL](https://doi.org/10.1007/978-94-007-6970-0_3)

Ben Shneiderman. Human-Centered AI. Oxford: Oxford University Press, 2022.

Shneiderman, Ben. "Human-Centered AI." Issues in Science and Technology 37, no. 2 (2021): 56-61. [URL](https://issues.org/human-centered-ai/)

Shneiderman, Ben. "Tutorial: Human-Centered AI: Reliable, Safe and Trustworthy." IUI '21 Companion: 26th International Conference on Intelligent User Interfaces - Companion, April 14, 2021, 7-8. [URL](https://doi.org/10.1145/3397482.3453994)

George Margetis, Stavroula Ntoa, Margherita Antona, and Constantine Stephanidis. "Human-Centered Design of Artificial Intelligence." In Handbook of Human Factors and Ergonomics, edited by Gavriel Salvendy and Waldemar Karwowski, 5th ed., 1085-1106. John Wiley & Sons, 2021. [URL](https://onlinelibrary.wiley.com/doi/10.1002/9781119636113.ch42)