Our framework for responsible AI ensures safety, fairness, transparency, and accountability across all machine learning capabilities.
All AI/ML models undergo rigorous validation before deployment. Clinical decision-support outputs are always surfaced as recommendations, never autonomous actions.
Model outputs include explanation metadata. Clinicians can inspect the reasoning chain behind any AI-generated suggestion or risk score.
Continuous fairness testing across demographic groups. Bias reports are generated quarterly and reviewed by the AI Ethics Committee.
No AI output triggers clinical action without clinician review and explicit approval. Override paths are always available and audited.
AI/ML models are classified by risk tier (Low / Medium / High / Critical). Higher-risk models require additional validation, monitoring, and approval gates.
Model drift, performance degradation, and output distribution shifts are monitored in real-time with automated alerts and rollback capabilities.
| Control | Frequency | Owner |
|---|---|---|
| Model Risk Classification | Per deployment | AI Ethics Committee |
| Bias & Fairness Audit | Quarterly | Data Science Lead |
| Model Performance Review | Monthly | ML Engineering |
| Training Data Governance Review | Quarterly | Privacy Officer |
| Explainability Validation | Per release | Clinical Informatics |
| Adverse Event Review | As needed | AI Ethics Committee |
| Third-party AI Vendor Assessment | Annual | Security Committee |
| Regulatory Compliance Check | Quarterly | Compliance Officer |
Risk tier classification, data governance review, ethics screening
Bias testing, performance benchmarks, explainability validation
Staged rollout, real-time drift monitoring, clinician feedback loops
Quarterly performance review, retraining decisions, decommission protocols