Responsible AI, security, and governance
Responsible AI, security, and governance tests whether you can deliver models that are fair, explainable, private, secure, and auditable. In Azure Machine Learning, use the Responsible AI dashboard for error analysis, fairness, interpretability, and counterfactuals; use security controls such as managed identity, private endpoints, customer-managed keys, and RBAC; use registry tags, approvals, and audit logs for governance.
Core rules
- Assess harms early: who is affected, what data is sensitive, and which groups need fairness review.
- Document model purpose, data provenance, evaluation results, limitations, and mitigation in a scorecard.
- Apply least privilege: separate data scientist, deployer, and approver roles; never embed secrets.
- Protect data in transit and at rest; use private endpoints and managed virtual networks for regulated data.
Worked example
A credit-limit model is checked with the Responsible AI dashboard. Fairness analysis shows higher false-negative rates for one age band. The team investigates proxy features, removes an unlawful proxy, retrains, and documents the residual gap. Access is restricted with RBAC, the workspace uses a private endpoint, and the model is registered with an approval tag before deployment.
Traps
- Treating responsible AI as a one-time report.
- Using sensitive attributes without a lawful basis.
- Overpermissioning service principals.
- Logging PII in telemetry.
- Deploying a model without an owner or documented limitations.