Performance Requirements

Define a minimum acceptable level of accuracy or reliability that an AI system must meet to be deployed. These requirements should be set based on the importance and potential risks of the task. For example, a system providing medical advice or financial risk assessments will require a higher performance threshold than a content recommendation engine.

Performance thresholds should be defined during system design and enforced throughout the system lifecycle. If models are retrained automatically, checks should be put in place to ensure that any updated version still meets the required threshold before being deployed. Consider tracking metrics such as accuracy, F1 score, or other domain-specific indicators depending on the use case.

- Prevents Deployment of Underperforming Models: Ensures AI systems meet quality standards before use in real-world settings.
- Maintains Long-Term Model Quality: Detects and blocks automatic updates that would reduce system performance.
- Supports Compliance Requirements: Provides measurable thresholds for demonstrating that deployed AI systems meet legal and regulatory standards (e.g. EU AI Act, NIST AI RMF).
- Improves User Trust: Builds confidence in AI decisions by ensuring consistency and transparency around system performance.

- Performance Threshold Documentation
- Internal documents or design specs defining the minimum required metrics for deployment (e.g. 90% F1 score for classification tasks).
- Automated Deployment Checks
- Screenshots or logs showing that models failing evaluation metrics are blocked from release.
- Evaluation Logs Over Time
- Records of periodic testing that show whether the model’s performance degrades post-deployment.
- Stakeholder Sign-off Records
- Internal sign-off forms showing approval of performance benchmarks and thresholds before production use.