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NAIC Model AI Bulletin

04/May/2026
json [ { "question": "What is human-in-the-loop in AI governance?", "answer": "Human-in-the-loop AI governance is a control that routes low-confidence model outputs to a person for review before a decision takes effect, with logs, time-on-task data, and approval-rate monitoring." }, { "question": "Why does automation bias defeat human oversight?", "answer": "Automation bias is the tendency to trust a polished machine output more than the reviewer's own judgment, which pushes approval rates toward 100 percent and erases the value of the review." }, { "question": "How do US frameworks address automation bias?", "answer": "US frameworks address automation bias by expecting documented, meaningful human review on high-stakes AI decisions, with NIST AI RMF, FCRA, NAIC, and state laws as the lead references." }, { "question": "What review architecture prevents rubber-stamp approval?", "answer": "Review architecture prevents rubber-stamp approval through five design patterns: friction by design, minimum review time, approval-rate health metrics, structured justification, and escalation paths for edge cases." }, { "question": "How do confidence thresholds decide what routes to a human?", "answer": "Confidence thresholds set a score below which an AI output routes to human review, calibrated per risk tier so high-stakes decisions get tighter thresholds and lower automation rates." } ] illustration json [ { "question": "What is human-in-the-loop in AI governance?", "answer": "Human-in-the-loop AI governance is a control that routes low-confidence model outputs to a person for review before a decision takes effect, with logs, time-on-task data, and approval-rate monitoring." }, { "question": "Why does automation bias defeat human oversight?", "answer": "Automation bias is the tendency to trust a polished machine output more than the reviewer's own judgment, which pushes approval rates toward 100 percent and erases the value of the review." }, { "question": "How do US frameworks address automation bias?", "answer": "US frameworks address automation bias by expecting documented, meaningful human review on high-stakes AI decisions, with NIST AI RMF, FCRA, NAIC, and state laws as the lead references." }, { "question": "What review architecture prevents rubber-stamp approval?", "answer": "Review architecture prevents rubber-stamp approval through five design patterns: friction by design, minimum review time, approval-rate health metrics, structured justification, and escalation paths for edge cases." }, { "question": "How do confidence thresholds decide what routes to a human?", "answer": "Confidence thresholds set a score below which an AI output routes to human review, calibrated per risk tier so high-stakes decisions get tighter thresholds and lower automation rates." } ] illustration

Human-in-the-Loop AI Governance: Beyond Rubber Stamps

Human-in-the-loop AI governance fails when reviewers rubber-stamp outputs. Here is the review architecture that makes oversight meaningful under US rules.
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