NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 4.2

MEASURE 4.2: Measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended. Results are documented.

About:
Feedback captured from relevant AI Actors can be evaluated in combination with output from Measure 2.5 to 2.11 to determine if the AI system is performing within pre-defined operational limits for validity and reliability, safety, security and resilience, privacy, bias and fairness, explainability and interpretability, and transparency and accountability. This feedback provides an additional layer of insight about AI system performance, including potential misuse or reuse outside of intended settings.

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

Suggested Actions:
- Integrate feedback from end users, operators, and affected individuals and communities from Map function as inputs to assess AI system trustworthiness characteristics. Ensure both positive and negative feedback is being assessed.
- Evaluate feedback in connection with AI system trustworthiness characteristics from Measure 2.5 to 2.11.
- Evaluate feedback regarding end user satisfaction with, and confidence in, AI system performance including whether output is considered valid and reliable, and explainable and interpretable.
- Identify mechanisms to confirm/support AI system output (e.g. recommendations), and end user perspectives about that output.
- Measure frequency of AI systems' override decisions, evaluate and document results, and feed insights back into continual improvement processes.
- Consult AI actors in impact assessment, human factors and socio-technical tasks to assist with analysis and interpretation of results.

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

- To what extent does the system/entity consistently measure progress towards stated goals and objectives?
- What policies has the entity developed to ensure the use of the AI system is consistent with its stated values and principles?
- To what extent are the model outputs consistent with the entity's values and principles to foster public trust and equity?
- Given the purpose of the AI, what level of explainability or interpretability is required for how the AI made its determination?
- To what extent can users or parties affected by the outputs of the AI system test the AI system and provide feedback?

### 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:
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)

Caitlin Thompson. "Who's Homeless Enough for Housing? In San Francisco, an Algorithm Decides." Coda, September 21, 2021. [URL](https://www.codastory.com/authoritarian-tech/san-francisco-homeless-algorithm/)

John Zerilli, Alistair Knott, James Maclaurin, and Colin Gavaghan. "Algorithmic Decision-Making and the Control Problem." Minds and Machines 29, no. 4 (December 11, 2019): 555-78. [URL](https://doi.org/10.1007/s11023-019-09513-7)

Fry, Hannah. Hello World: Being Human in the Age of Algorithms. New York: W.W. Norton & Company, 2018. [URL](https://wwnorton.com/books/Hello-World)

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)

Diane Hart, Gabi Diercks-O'Brien, and Adrian Powell. "Exploring Stakeholder Engagement in Impact Evaluation Planning in Educational Development Work." Evaluation 15, no. 3 (2009): 285-306. [URL](https://doi.org/10.1177/1356389009105882)

Asit Bhattacharyya and Lorne Cummings. "Measuring Corporate Environmental Performance - Stakeholder Engagement Evaluation." Business Strategy and the Environment 24, no. 5 (2013): 309-25. [URL](https://doi.org/10.1002/bse.1819)

Hendricks, Sharief, Nailah Conrad, Tania S. Douglas, and Tinashe Mutsvangwa. "A Modified Stakeholder Participation Assessment Framework for Design Thinking in Health Innovation." Healthcare 6, no. 3 (September 2018): 191-96. [URL](https://doi.org/10.1016/j.hjdsi.2018.06.003)

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)

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)