NIST AI RMF Playbook
AI RMF Playbook > Map > MAP 5.1

MAP 5.1: Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented.

About:
AI actors can evaluate, document and triage the likelihood of AI system impacts identified in Map 5.1 Likelihood estimates may then be assessed and judged for go/no-go decisions about deploying an AI system. If an organization decides to proceed with deploying the system, the likelihood and magnitude estimates can be used to assign TEVV resources appropriate for the risk level.

Suggested Actions:
- Establish assessment scales for measuring AI systems' impact. Scales may be qualitative, such as red-amber-green (RAG), or may entail simulations or econometric approaches. Document and apply scales uniformly across the organization's AI portfolio.
- Apply TEVV regularly at key stages in the AI lifecycle, connected to system impacts and frequency of system updates.
- Identify and document likelihood and magnitude of system benefits and negative impacts in relation to trustworthiness characteristics.
- Establish processes for red teaming to identify and connect system limitations to AI lifecycle stage(s) and potential downstream impacts

Transparency and Documentation:
### Organizations can document the following
- Which population(s) does the AI system impact?
- What assessments has the entity conducted on trustworthiness characteristics for example data security and privacy impacts associated with the AI system?
- Can the AI system be tested by independent third parties?

### AI Transparency Resources
- Datasheets for Datasets. [URL](http://arxiv.org/abs/1803.09010)
- GAO-21-519SP: AI Accountability Framework for Federal Agencies & Other Entities. [URL](https://www.gao.gov/products/gao-21-519sp)
- AI policies and initiatives, in Artificial Intelligence in Society, OECD, 2019. [URL](https://www.oecd.org/publications/artificial-intelligence-in-society-eedfee77-en.htm)
- Intel.gov: AI Ethics Framework for Intelligence Community - 2020. [URL](https://www.intelligence.gov/artificial-intelligence-ethics-framework-for-the-intelligence-community)
- Assessment List for Trustworthy AI (ALTAI) - The High-Level Expert Group on AI - 2019. [LINK](https://altai.insight-centre.org/), [URL](https://digital-strategy.ec.europa.eu/en/library/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment)

References:
Emilio Gómez-González and Emilia Gómez. 2020. Artificial intelligence in medicine and healthcare. Joint Research Centre (European Commission). [URL](https://op.europa.eu/en/publication-detail/-/publication/b4b5db47-94c0-11ea-aac4-01aa75ed71a1/language-en)

Artificial Intelligence Incident Database. 2022. [URL](https://incidentdatabase.ai/?lang=en)

Anthony M. Barrett, Dan Hendrycks, Jessica Newman and Brandie Nonnecke. "Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks". ArXiv abs/2206.08966 (2022) [URL](https://arxiv.org/abs/2206.08966)

Ganguli, D., et al. (2022). Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. arXiv. https://arxiv.org/abs/2209.07858

Upol Ehsan, Q. Vera Liao, Samir Passi, Mark O. Riedl, and Hal Daumé. 2024. Seamful XAI: Operationalizing Seamful Design in Explainable AI. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 119. https://doi.org/10.1145/3637396