MS-4.2 Measurement results regarding AI system trustworthiness in deployment contexts and across the AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended, and results are documented
Measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing
system holds itEvidence a system already holds
none for this control
periodic reviewEvidence produced at each review
none for this control
governing documentDocuments that govern the control
- Measurement results with domain expert and AI actor input recorded against them · Document repository
- The pre-defined operational limits the results are judged against · Document repository
- Documentation of whether the system is performing consistently as intended · Document repository
- Disposition of any expert view that conflicted with the measured result · Document repository
First move
Common gaps auditors find
- Results published with no expert validation of what they mean in context
- Operational limits set after the results were known
- Conflicting expert judgement recorded and then disregarded without reasoning
Do this for your whole sheet
Paste the rows you run your controls from and get this mapping for every control at once, with the periodic-review ones flagged and a first move per row. No account for the first run.
Build my evidence sheetMS-4.1 Measurement approaches for identifying AI risks are connected to deployment contexts and informed through consultation with domain experts and other end users, and approaches are documented · MS-4.3 Measurable performance improvements or declines based on consultations with relevant AI actors including affected communities, and field data about context-relevant risks and trustworthiness characteristics, are identified and documented