NIST AI RMF Playbook
AI RMF Playbook > Manage > MANAGE 1.1

MANAGE 1.1: A determination is made as to whether the AI system achieves its intended purpose and stated objectives and whether its development or deployment should proceed.

About:
AI systems may not necessarily be the right solution for a given business task or problem. A standard risk management practice is to formally weigh an AI system's negative risks against its benefits, and to determine if the AI system is an appropriate solution. Tradeoffs among trustworthiness characteristics -such as deciding to deploy a system based on system performance vs system transparency-may require regular assessment throughout the AI lifecycle.

Suggested Actions:
- Consider trustworthiness characteristics when evaluating AI systems' negative risks and benefits.
- Utilize TEVV outputs from map and measure functions when considering risk treatment.
- Regularly track and monitor negative risks and benefits throughout the AI system lifecycle including in post-deployment monitoring.
- Regularly assess and document system performance relative to trustworthiness characteristics and tradeoffs between negative risks and opportunities.
- Evaluate tradeoffs in connection with real-world use cases and impacts and as enumerated in Map function outcomes.

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

- How do the technical specifications and requirements align with the AI system's goals and objectives?
- To what extent are the metrics consistent with system goals, objectives, and constraints, including ethical and compliance considerations?
- What goals and objectives does the entity expect to achieve by designing, developing, and/or deploying 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)
- 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:
Arvind Narayanan. How to recognize AI snake oil. Retrieved October 15, 2022. [URL](https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf)

Board of Governors of the Federal Reserve System. SR 11-7: Guidance on Model Risk Management. (April 4, 2011). [URL](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm)

Emanuel Moss, Elizabeth Watkins, Ranjit Singh, Madeleine Clare Elish, Jacob Metcalf. 2021. Assembling Accountability: Algorithmic Impact Assessment for the Public Interest. (June 29, 2021). [URL](https://ssrn.com/abstract=3877437 or http://dx.doi.org/10.2139/ssrn.3877437)

Fraser, Henry L and Bello y Villarino, Jose-Miguel, Where Residual Risks Reside: A Comparative Approach to Art 9(4) of the European Union's Proposed AI Regulation (September 30, 2021). [LINK](https://ssrn.com/abstract=3960461), [URL](http://dx.doi.org/10.2139/ssrn.3960461)

Microsoft. 2022. Microsoft Responsible AI Impact Assessment Template. (June 2022). [URL](https://blogs.microsoft.com/wp-content/uploads/prod/sites/5/2022/06/Microsoft-RAI-Impact-Assessment-Template.pdf)

Office of the Comptroller of the Currency. 2021. Comptroller's Handbook: Model Risk Management, Version 1.0, August 2021. [URL](https://www.occ.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/index-model-risk-management.html)

Solon Barocas, Asia J. Biega, Benjamin Fish, et al. 2020. When not to design, build, or deploy. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 695. [URL](https://doi.org/10.1145/3351095.3375691)