Threshold Setting
Defining boundaries or cut-off values in AI decision rules (e.g., confidence scores) to balance risks like false positives versus false negatives.
Involves choosing decision thresholds - confidence, probability, or score cut-offs - that determine when an automated action is taken versus when human review is required. Thresholds are set based on risk tolerance, cost-benefit analyses, and stakeholder preferences. Governance processes require threshold-setting to be documented, tested across subgroups, and periodically recalibrated as conditions change, ensuring optimal trade-offs.
A medical-diagnosis AI flags scans for biopsy recommendations only if its confidence exceeds 90%, reducing false alarms. Scans with 70–90% confidence are routed to radiologists for review. Quarterly calibration ensures these thresholds maintain a target under-diagnosis rate below 1% and over-diagnosis rate below 5%.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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