Japan AI Guidelines JP-AIG-Continuous-Monitoring-Lifecycle-Model-Evaluation-Performance-Drift-Post-Deployment: Japan AI Guidelines Continuous Monitoring + AI System Lifecycle Management + Model Evaluation + Performance Drift + Concept Drift + Post-Deployment + Retraining Triggers + Safe Update + Decommissioning + Model Card Versioning

Continuous Monitoring + Lifecycle Management is essential to ongoing trustworthy AI per Japan AI Guidelines for Business + integrates Safety + Accountability + Transparency Principles + addresses post-deployment risks. (1) AI System Lifecycle Stages: (a) Conception + Design - requirements + risk + ethics review; (b) Development + Training - data + bias + safety; (c) Validation + Testing - benchmarks + red-team + AISI evaluation where applicable; (d) Pre-Production - sandboxing + limited release; (e) Deployment - phased + monitoring + human oversight; (f) Operation + Monitoring - performance + drift + incident + harm; (g) Update + Retraining - safety re-evaluation; (h) Decommissioning - data deletion + model archive + downstream notification. (2) Performance Monitoring: (a) Accuracy + Precision + Recall + F1 across subgroups; (b) Calibration metrics; (c) Latency + Throughput; (d) Coverage + Confidence; (e) Per-Use-Case KPIs; (f) Per-Subgroup performance (fairness monitoring); (g) Hallucination rate (LLM); (h) Refusal rate (safety guardrail effectiveness). (3) Concept + Distribution Drift Detection: (a) Population Stability Index (PSI); (b) Kullback-Leibler Divergence; (c) Kolmogorov-Smirnov Test; (d) Maximum Mean Discrepancy (MMD); (e) Wasserstein Distance; (f) Per-Feature monitoring; (g) Output distribution monitoring; (h) Drift severity scoring + alerting. (4) Bias + Fairness Monitoring: (a) Per-subgroup performance; (b) Demographic parity tracking; (c) Equalised odds tracking; (d) Calibration per group; (e) Bias incident rate; (f) Fairness regression detection; (g) Stakeholder feedback integration. (5) Safety + Harm Monitoring: (a) AI Incident tracking - severity + frequency; (b) Harmful output rate; (c) Misuse pattern detection; (d) User complaint analysis; (e) Adversarial attack detection; (f) Reputational risk monitoring; (g) Litigation risk monitoring. (6) Post-Deployment Evaluation: (a) A/B testing + canary deployment; (b) Shadow mode evaluation; (c) Champion-Challenger model comparison; (d) Feedback loop with users + reviewers; (e) Periodic re-validation against current data; (f) Stakeholder satisfaction surveys; (g) External audit (annual or per-release for high-risk). (7) Retraining Triggers: (a) Performance degradation below threshold; (b) Concept drift detected; (c) Bias regression; (d) Safety incident; (e) New data availability; (f) Regulatory + sector guidance change; (g) Foundation model upgrade; (h) Periodic schedule (quarterly + annually). (8) Safe Update Practices: (a) Re-validation of safety + fairness + performance; (b) Re-Documentation - new Model Card + System Card version; (c) DPIA refresh; (d) Stakeholder notification of material changes; (e) Phased rollout + canary deployment; (f) Rollback capability; (g) Version control + reproducibility; (h) Audit trail. (9) Decommissioning Procedure: (a) Decision criteria - performance + cost + replacement availability; (b) Stakeholder notification (users + downstream systems + regulators); (c) Data archival or deletion per retention policy; (d) Model archive (research) or destruction; (e) Personal information erasure rights; (f) Documentation retention; (g) Replacement migration plan; (h) Post-decommission monitoring (transition period). (10) Model Card + System Card Versioning: (a) Per-version Model Card + System Card; (b) Change log + diff documentation; (c) Stakeholder communication of material changes; (d) Versioned API documentation; (e) Reproducibility - model + code + data + config snapshot; (f) Long-Term Validation of evaluation results; (g) Registry of all versions. (11) Monitoring Infrastructure: (a) Observability platform (Prometheus + Grafana + Datadog + custom AI dashboards); (b) ML Monitoring tools (Arize + Fiddler + WhyLabs + Evidently + AWS SageMaker Model Monitor + Vertex AI + Azure ML monitoring); (c) Log aggregation + analysis; (d) Alert management + escalation; (e) Incident management integration; (f) Dashboard for senior management + Board reporting. (12) Industry-Specific Lifecycle Practices: (a) Healthcare - PMDA approval per version + ongoing post-market surveillance; (b) Financial - FSA model risk management + back-testing + stress testing; (c) Autonomous Vehicles - MLIT regulation + continuous learning + over-the-air updates; (d) Critical Infrastructure - METI guidelines + downtime windows. Coordinates with NIST AI RMF Manage.4 + ISO/IEC 42001 AI MS + ISO/IEC 23053 + ISO/IEC TR 24028 + AISI + EU AI Act Article 17 Quality Management System + Article 72 Post-Market Monitoring + PMDA + FSA + MLIT + MHLW + sector regulators + ML Monitoring vendor ecosystem + Datasheets for Datasets + Model Cards + System Cards + Reproducibility tools (MLflow + DVC + Weights and Biases). Japan AI Guidelines Continuous Monitoring + Lifecycle applies.