MAP-2.3 Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct validation.
What the standard requires
Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection(e.g., availability, representativeness, suitability), system trustworthiness, and construct validation.
Quoted verbatim from NIST AI Risk Management Framework (AI RMF 1.0), which is in the public domain.
Maps to 1 control in 1 other framework
| Framework | Control |
|---|---|
| ISO/IEC 42001:2023 | A.6.2.4 AI-System Verification and Validation |
Mappings are CMDash’s own, seeded with the product and shown on both frameworks they connect. In the product they mean evidence you attach here also counts against the controls listed above.
Integrations that carry evidence for this family
These connectors produce data relevant to NIST AI Risk Management Framework 1.0’s MAP family, which is the granularity CMDash publishes — there is no pre-built per-control mapping, and claiming one would be inventing it. You decide which evidence answers which control.
In CMDash this control is a row with an owner, a status, uploaded evidence kept with its full history, and an export an assessor can read.