NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 3.3

MEASURE 3.3: Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics.

About:
Assessing impact is a two-way effort. Many AI system outcomes and impacts may not be visible or recognizable to AI actors across the development and deployment dimensions of the AI lifecycle, and may require direct feedback about system outcomes from the perspective of end users and impacted groups.

Feedback can be collected indirectly, via systems that are mechanized to collect errors and other feedback from end users and operators

Metrics and insights developed in this sub-category feed into Manage 4.1 and 4.2.

Suggested Actions:
- Measure efficacy of end user and operator error reporting processes.
- Categorize and analyze type and rate of end user appeal requests and results.
- Measure feedback activity participation rates and awareness of feedback activity availability.
- Utilize feedback to analyze measurement approaches and determine subsequent courses of action.
- Evaluate measurement approaches to determine efficacy for enhancing organizational understanding of real world impacts.
- Analyze end user and community feedback in close collaboration with domain experts.

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

- To what extent can users or parties affected by the outputs of the AI system test the AI system and provide feedback?
- Did your organization address usability problems and test whether user interfaces served their intended purposes?
- How easily accessible and current is the information available to external stakeholders?
- 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)
- 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:
Sasha Costanza-Chock. Design Justice: Community-Led Practices to Build the Worlds We Need. Cambridge: The MIT Press, 2020. [URL](https://direct.mit.edu/books/book/4605/Design-JusticeCommunity-Led-Practices-to-Build-the)

David G. Robinson. Voices in the Code: A Story About People, Their Values, and the Algorithm They Made. New York: Russell Sage Foundation, 2022. [URL](https://www.russellsage.org/publications/voices-code)

Fernando Delgado, Stephen Yang, Michael Madaio, and Qian Yang. "Stakeholder Participation in AI: Beyond 'Add Diverse Stakeholders and Stir.'" arXiv preprint, submitted November 1, 2021. [URL](https://arxiv.org/abs/2111.01122)

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)

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

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)

Batya Friedman, Peter H. Kahn, Jr., and Alan Borning. "Value Sensitive Design: Theory and Methods." University of Washington Department of Computer Science & Engineering Technical Report 02-12-01, December 2002. [URL](https://faculty.washington.edu/pkahn/articles/vsd-theory-methods-tr.pdf)

Emanuel Moss, Elizabeth Watkins, Ranjit Singh, Madeleine Clare Elish, and Jacob Metcalf. "Assembling Accountability: Algorithmic Impact Assessment for the Public Interest." SSRN, July 8, 2021. [URL](https://doi.org/10.2139/ssrn.3877437)

Alexandra Reeve Givens, and Meredith Ringel Morris. "Centering Disability Perspectives in Algorithmic Fairness, Accountability, & Transparency." FAT* '20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, January 27, 2020, 684-84. [URL](https://doi.org/10.1145/3351095.3375686)