Assess whether the system is high-risk under the EU AI Act (208972)

SITUATION Model-risk officer is responsible for the system is high-risk in a hospital deploying a sepsis-risk model, using hiring-tool adverse-impact tables as the only working extract. A denied applicant requesting the principal reasons is what reset the timeline for this AI Governance Bias and Training Data file.

DECISION Model-risk officer in a hospital deploying a sepsis-risk model must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using hiring-tool adverse-impact tables after a denied applicant requesting the principal reasons.

HYPOTHESES TO TEST 1. Hiring-tool adverse-impact tables reads as Policy or governance breach once a denied applicant requesting the principal reasons is lined up to the same AI Governance population. 2. Hiring-tool adverse-impact tables is closer to Model defect after a denied applicant requesting the principal reasons; Policy or governance breach would over-claim this Bias and Training Data extract. 3. Dual failure is still live in hiring-tool adverse-impact tables for model-risk officer in a hospital deploying a sepsis-risk model. 4. Hiring-tool adverse-impact tables is missing the fact model-risk officer needs after a denied applicant requesting the principal reasons; stop this AI Governance close.

ANALYSIS REQUIRED 1. Verify data provenance and the human-oversight gate model-risk officer can actually point to. 2. Walk the model input/output path recorded in hiring-tool adverse-impact tables and mark each hop approved, shadow, or unlogged. 3. Verify data provenance and the human-oversight gate model-risk officer can actually point to. 4. For this AI Governance Bias and Training Data file, read hiring-tool adverse-impact tables against a denied applicant requesting the principal reasons and write the one fact that would move the system is high-risk for model-risk officer.

RECOMMENDATION Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance / Bias and Training Data packet (hiring-tool adverse-impact tables after a denied applicant requesting the principal reasons). The follow-on Bias and Training Data action is what model-risk officer 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 hiring-tool adverse-impact tables, then the action for model-risk officer - Hypothesis scorecard against hiring-tool adverse-impact tables: supported / rejected / untestable - Owner and next date for model-risk officer in a hospital deploying a sepsis-risk model - What changes the system is high-risk if a denied applicant requesting the principal reasons is later withdrawn