AI Evaluation Metrics - Error Rate

Definition:
The percentage of incorrect, inappropriate, or harmful responses generated by the AI model.

Purpose:
Minimize incorrect or unsafe outputs that could mislead users or cause harm, especially critical in healthcare contexts.

For Compliance Team:

Set Error Thresholds: Define maximum tolerated error rates (e.g., ≤10% for wellness, ≤3% for pharmacies).
Incident Tracking: Maintain logs of errors, classify severity, and ensure timely review and remediation.
User Reporting: Encourage users to report errors and have a system to escalate critical cases immediately.
Compliance Audits: Include error rate analysis in regular compliance reviews and regulatory filings.
Risk Mitigation: Develop contingency plans for managing and mitigating impacts of high error rates.

For Engineers:

Automated Testing: Build comprehensive test suites covering typical and edge cases to detect errors early.
Error Logging: Implement detailed logging for all responses flagged as errors or low confidence.
Confidence Thresholds: Use model confidence scores to filter or flag uncertain responses for human review.
Continuous Improvement: Analyze error patterns to identify root causes and retrain or refine models accordingly.
Human-in-the-Loop: Set up processes for human intervention on flagged responses, especially for high-risk advice.