NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MP-5.1: Likelihood and magnitude of each identified impact are identified and documented, based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team, or other data
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. Each identified impact carries a likelihood and a magnitude derived from stated evidence, including comparable past deployments and public incident reports, not from unsupported estimation.
Maintained by Gerard Blokdyk·Verified against the published standard ·Control text last updated
What else in your programme already covers this
This control maps to 3 controls across 2 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
You are reading one control. How much of NIST AI Risk Management Framework (AI RMF 1.0) have you already done?
NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MP-5.1 is one control. If you already hold one of the frameworks below, a reviewed crosswalk already says how much of NIST AI Risk Management Framework (AI RMF 1.0) your existing evidence covers. Hold EU AI Act and 48 of 72 NIST AI Risk Management Framework (AI RMF 1.0) controls already carry evidence.
Each report names every control your existing framework evidences, every one it does not, the reasoning behind each claim, and the claims that were argued against and rejected. 10 were rejected on the EU AI Act pair alone.