AI evaluation and blocked-action program

What problem or claim is examined

Unsupported, ambiguous, or access-violating output may reach users or downstream systems without adequate measurement or authority.

System or demonstration context

An AI reviewer, copilot, or agent produces useful demonstrations, but acceptable failure, human review, and prohibited downstream actions remain undefined.

Source state and inputs

• No production customer data
• No safety certification
• No claim of error elimination

Method

• Golden and adversarial cases
• Failure taxonomy
• Reviewer calibration
• Blocked-action controls
• Release scorecards

Representative cases

• Define task and risk categories
• Build representative cases
• Separate failure types
• Calibrate automated measures against human review
• Set release and rollback thresholds
• Log blocked actions and overrides

Observed result or demonstration output

Shows how an AI pilot can be converted into an inspectable proceed, narrow, remediate, or stop decision.

Human review and authority

Demonstrates a release-evidence and human-authority pattern.

Decision supported

Shows how an AI pilot can be converted into an inspectable proceed, narrow, remediate, or stop decision.

Artifacts retained

• Evaluation dataset
• Failure taxonomy
• Calibration report
• Reviewer worksheet
• Release scorecard
• Operational runbook

Limitations and boundary

This is an evaluation pattern, not a formal audit, safety certification, or guarantee that a system will be error-free.