Classification
Risk & Compliance
Overview
Post-market monitoring (PMM) is the set of processes used to observe, measure, and improve AI systems after deployment. Because operating environments change, data distributions drift, and user behaviors evolve, models that performed well in testing can degrade or exhibit new failure modes. PMM includes telemetry collection, performance and fairness dashboards, incident intake, complaint handling, retraining triggers, documentation updates, and governance reviews. Effective PMM defines thresholds, roles, and escalation paths, linking production signals to remediation actions. Limitations include noisy metrics, blind spots in data capture, and organizational friction between product, compliance, and engineering teams. Mature programs integrate PMM with model registries, change management, and rollback procedures to ensure that fixes are auditable and that high-risk systems remain safe and compliant throughout their lifecycle.
Governance Context
The EU AI Act mandates post-market monitoring for high-risk AI systems, including obligations to collect, document, and analyze performance and serious incidents, and to cooperate with market surveillance authorities. Providers must update technical documentation and, where necessary, take corrective actions or withdraw systems. ISO/IEC 23894:2023 requires continuous risk assessment and monitoring controls; NIST AI RMF emphasizes ongoing measurement, logging, and incident response. Two concrete obligations are: (1) implement automated monitoring for performance, drift, and safety with defined thresholds that trigger human review and documented corrective actions; and (2) maintain auditable logs and versioned documentation (model cards, change logs, retraining records) to demonstrate compliance and support external investigations.
Ethical & Societal Implications
PMM protects users by catching harm earlier, supporting fairness maintenance and safety in changing environments. It also raises governance questions: who owns remediation decisions, how are trade-offs recorded, and how are affected communities informed? Transparent reporting and meaningful human oversight improve societal trust, while inadequate PMM can entrench bias or allow unsafe behavior to persist.
Key Takeaways
AI performance and risks change after deployment; monitoring is essential.; EU AI Act, NIST, and ISO require continuous measurement and documentation.; Define thresholds, roles, and escalation paths tied to corrective actions.; Maintain auditable logs, model cards, and retraining records.; PMM integrates with drift detection, incident response, and change control.