Assess whether explainability artifacts would survive an exam (91ae5e)

Situation

Training-data provenance questionnaire arrived with a near-miss where an agent emailed a customer unreviewed for chief AI officer. That is a AI Governance Policy and Oversight decision on explainability artifacts would survive in a city using a hiring-screen algorithm.

Decision

Chief AI officer in a city using a hiring-screen algorithm 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

• Chief AI officer 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.
• Chief AI officer 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.
• A near-miss where an agent emailed a customer unreviewed never reached the population in training-data provenance questionnaire — reopen intake, do not close explainability artifacts would survive.
• Two facts in training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed conflict for chief AI officer; hold this Policy and Oversight file.

Analysis required

• Map the approved-use case to the system explainability artifacts would survive would bind.
• Check intended purpose and inventory status against EU AI Act / exam-readiness language after a near-miss where an agent emailed a customer unreviewed.
• Map the approved-use case to the system explainability artifacts would survive would bind.
• 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 explainability artifacts would survive for chief AI officer.

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). If training-data provenance questionnaire cannot force a AI Governance label under Policy and Oversight, stop. Do not invent pages a city using a hiring-screen algorithm does not have.

Command returns

• Bottom-line AI Governance option on explainability artifacts would survive, then the evidence in training-data provenance questionnaire, then the action for chief AI officer
• 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