Continuous learning and adaptation of AI systems
Description
Controlled processes for AI systems that learn from operational data, including validation of learned behaviors and prevention of harmful adaptation.
Implementation Guidance
Continuous Learning Policy
Define organizational policy on continuous learning: which AI systems are permitted to learn from operational data, what safeguards are required, approval requirements for enabling continuous learning, and prohibited adaptation scenarios (learning from biased feedback, reinforcing harmful patterns).
Validation Gates
Implement validation checkpoints for continuously learning systems: automated performance and fairness regression tests before deploying updated models, comparison against baseline model performance, human review of learned behavior samples, and rollback triggers if metrics degrade.
Drift Prevention
Protect against harmful adaptation: implement learning rate constraints, maintain golden test sets that must always pass, monitor for feedback loops (model bias reinforcing biased data collection), and set boundaries on how far the model can drift from its validated baseline.
Evidence Requirements
- Continuous learning policy and approved systems list
- Validation gate configurations and test results
- Model update logs with pre/post performance comparison
- Drift monitoring records and rollback history