MS-3.3 Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics
Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics. End users and impacted communities have a working
system holds itEvidence a system already holds
- The integration of that feedback into evaluation metrics · Policy repository / GRC workspace
periodic reviewEvidence produced at each review
- Records of problems reported and appeals lodged, with outcomes · Document repository
- Evidence the route is discoverable by the people expected to use it · Document repository
governing documentDocuments that govern the control
- The reporting and appeal route available to end users and impacted communities · Document repository
First move
Common gaps auditors find
- Appeal route exists in policy but is not reachable from the point of the decision
- Reports handled as customer service tickets with no route into evaluation
- No appeal available where the decision is automated and consequential
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-3.2 Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available · MS-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