Assess whether the system is high-risk under the EU AI Act (ff8db7)

SITUATION Training-data provenance questionnaire arrived with a near-miss where an agent emailed a customer unreviewed for EU AI Act implementation manager. That is a AI Governance Policy and Oversight decision on the system is high-risk in an insurer scoring claims with a third-party model.

DECISION EU AI Act implementation manager in an insurer scoring claims with a third-party model must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed.

HYPOTHESES TO TEST 1. EU AI Act implementation manager can defend Policy or governance breach from training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed in a AI Governance challenge. 2. EU AI Act implementation manager cannot defend Policy or governance breach from training-data provenance questionnaire; Model defect is what the extract actually supports after a near-miss where an agent emailed a customer unreviewed. 3. A near-miss where an agent emailed a customer unreviewed never reached the population in training-data provenance questionnaire — reopen intake, do not close the system is high-risk. 4. Two facts in training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed conflict for EU AI Act implementation manager; hold this Policy and Oversight file.

ANALYSIS REQUIRED 1. Verify data provenance and the human-oversight gate EU AI Act implementation manager can actually point to. 2. Walk the model input/output path recorded in training-data provenance questionnaire and mark each hop approved, shadow, or unlogged. 3. Verify data provenance and the human-oversight gate EU AI Act implementation manager can actually point to. 4. For this AI Governance Policy and Oversight file, read training-data provenance questionnaire against a near-miss where an agent emailed a customer unreviewed and write the one fact that would move the system is high-risk for EU AI Act implementation manager.

RECOMMENDATION Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance / Policy and Oversight packet (training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed). The follow-on Policy and Oversight action is what EU AI Act implementation manager does next: implement the option, assign an owner, and log the missing fact.

COMMAND RETURNS - Bottom-line AI Governance option on the system is high-risk, then the evidence in training-data provenance questionnaire, then the action for EU AI Act implementation manager - Hypothesis scorecard against training-data provenance questionnaire: supported / rejected / untestable - Policy and Oversight finding in training-data provenance questionnaire that a second reviewer can re-perform - Missing page in training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed, if any