NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 4.3

MEASURE 4.3: Measurable performance improvements or declines based on consultations with relevant AI actors including affected communities, and field data about context-relevant risks and trustworthiness characteristics, are identified and documented.

About:
TEVV activities conducted throughout the AI system lifecycle can provide baseline quantitative measures for trustworthy characteristics. When combined with results from Measure 2.5 to 2.11 and Measure 4.1 and 4.2, TEVV actors can maintain a comprehensive view of system performance. These measures can be augmented through participatory engagement with potentially impacted communities or other forms of stakeholder elicitation about AI systems' impacts. These sources of information can allow AI actors to explore potential adjustments to system components, adapt operating conditions, or institute performance improvements.

Suggested Actions:
- Develop baseline quantitative measures for trustworthy characteristics.
- Delimit and characterize baseline operation values and states.
- Utilize qualitative approaches to augment and complement quantitative baseline measures, in close coordination with impact assessment, human factors and socio-technical AI actors.
- Monitor and assess measurements as part of continual improvement to identify potential system adjustments or modifications
- Perform and document sensitivity analysis to characterize actual and expected variance in performance after applying system or procedural updates.
- Document decisions related to the sensitivity analysis and record expected influence on system performance and identified risks.

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

- To what extent are the model outputs consistent with the entity's values and principles to foster public trust and equity?
- How were sensitive variables (e.g., demographic and socioeconomic categories) that may be subject to regulatory compliance specifically selected or not selected for modeling purposes?
- Did your organization implement a risk management system to address risks involved in deploying the identified AI solution (e.g. personnel risk or changes to commercial objectives)?
- How will the accountable human(s) address changes in accuracy and precision due to either an adversary's attempts to disrupt the AI or unrelated changes in the operational/business environment?
- How will user and peer engagement be integrated into the model development process and periodic performance review once deployed?

### 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)