Errors in AI output are logged and communicated to strengthen awareness of AI risk.
Tier II
Optional extra depth on top of the baseline. Organizations with more mature AI governance, or more exposure, may choose to adopt these for more thorough control over AI than the baseline alone provides.
Why
AI output can be factually incorrect, miss critical context or hallucinate details. Most such errors do not need to be recorded, yet errors that appear in business-critical documents or processes can show the organization where AI systems fail in practice. Patterns become clear after collecting such errors and can act as powerful feedback to the organization and a reminder to employees to always check AI output or decisions.
How
The organization should facilitate a simple way to report AI errors, and should encourage employees to share errors and other negative feedback with the responsible person or team/department (see GV.2), or in a dedicated chat channel. This feedback should be considered as part of the AI risk review (see GV.4).
Sources
- NIST AI RMF GOVERN 4.3
- NIST AI RMF MANAGE 4.3
- ISO/IEC 42001 A.3.3