Building Infrastructure for High-Risk AI Systems Under the AI Act

If your AI system falls under Annex III of the AI Act — credit scoring, recruitment, medical devices, critical infrastructure management, law enforcement, education — you need infrastructure that satisfies Articles 8-15. Here's the architecture for each requirement.

Article 9: Risk Management System

A continuous, iterative process throughout the AI system lifecycle:

• Risk identification pipeline — Automated scanning for known risks: bias in training data, adversarial vulnerability, performance degradation on edge cases
• Risk quantification — Metrics dashboards showing risk levels across fairness, safety, robustness dimensions
• Risk mitigation tracking — Integration with issue tracking (Jira, Linear) for risk-to-remediation workflow
• Residual risk documentation — Automated capture of accepted risks with justification

Article 10: Data and Data Governance

• Data quality infrastructure — Great Expectations, Deequ, or custom validation pipelines running on every data ingestion
• Bias detection — Automated demographic parity, equalized odds, and calibration analysis on training datasets
• Data lineage — End-to-end tracking from source to feature to model prediction (Apache Atlas, OpenLineage)
• Annotation quality management — Inter-annotator agreement monitoring, annotation audit trails

Article 13: Transparency and Information

• Instructions for use generation — Auto-generated documentation of system capabilities, limitations, intended use
• Performance disclosure — Automated accuracy, precision, recall reporting across relevant subgroups
• Explainability infrastructure — SHAP, LIME, or attention-based explanations served alongside predictions

Article 14: Human Oversight

• Human-in-the-loop (HITL) infrastructure — Review queues, approval workflows, override interfaces
• Confidence-based routing — Low-confidence predictions automatically routed to human reviewers
• Kill switch infrastructure — Ability to disable AI system immediately, with fallback to non-AI process
• Override tracking — Log every human override for analysis and model improvement