NIST AI RMF Playbook
AI RMF Playbook > Manage > MANAGE 1.4

MANAGE 1.4: Negative residual risks (defined as the sum of all unmitigated risks) to both downstream acquirers of AI systems and end users are documented.

About:
Organizations may choose to accept or transfer some of the documented risks from MAP and MANAGE 1.3 and 2.1. Such risks, known as residual risk, may affect downstream AI actors such as those engaged in system procurement or use. Transparent monitoring and managing residual risks enables cost benefit analysis and the examination of potential values of AI systems versus its potential negative impacts.

Suggested Actions:
- Document residual risks within risk response plans, denoting risks that have been accepted, transferred, or subject to minimal mitigation.
- Establish procedures for disclosing residual risks to relevant downstream AI actors .
- Inform relevant downstream AI actors of requirements for safe operation, known limitations, and suggested warning labels as identified in MAP 3.4.

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

- What are the roles, responsibilities, and delegation of authorities of personnel involved in the design, development, deployment, assessment and monitoring of the AI system?
- Who will be responsible for maintaining, re-verifying, monitoring, and updating this AI once deployed?
- How will updates/revisions be documented and communicated? How often and by whom?
- How easily accessible and current is the information available to external stakeholders?

### 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)
- Datasheets for Datasets. [URL](https://arxiv.org/abs/1803.09010)

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)