NIST AI RMF Playbook
AI RMF Playbook > Manage > MANAGE 1.3

MANAGE 1.3: Responses to the AI risks deemed high priority as identified by the Map function, are developed, planned, and documented. Risk response options can include mitigating, transferring, avoiding, or accepting.

About:
Outcomes from GOVERN-1, MAP-5 and MEASURE-2, can be used to address and document identified risks based on established risk tolerances. Organizations can follow existing regulations and guidelines for risk criteria, tolerances and responses established by organizational, domain, discipline, sector, or professional requirements. In lieu of such guidance, organizations can develop risk response plans based on strategies such as accepted model risk management, enterprise risk management, and information sharing and disclosure practices.

Suggested Actions:
- Observe regulatory and established organizational, sector, discipline, or professional standards and requirements for applying risk tolerances within the organization.
- Document procedures for acting on AI system risks related to trustworthiness characteristics.
- Prioritize risks involving physical safety, legal liabilities, regulatory compliance, and negative impacts on individuals, groups, or society.
- Identify risk response plans and resources and organizational teams for carrying out response functions.
- Store risk management and system documentation in an organized, secure repository that is accessible by relevant AI Actors and appropriate personnel.

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

- Has the system been reviewed to ensure the AI system complies with relevant laws, regulations, standards, and guidance?
- To what extent has the entity defined and documented the regulatory environment-including minimum requirements in laws and regulations?
- 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)?

### 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)
- 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)