Designed for AI Quality Management System Managers, this course develops the specialized AI literacy required to support providers and deployers in fulfilling Article 4 obligations under the EU AI Act.
Course Outline:
AI Literacy for AI quality management system manager
Course Overview
This self-paced e-learning program equips AI quality management system manager with advance literacy to meet non-delegable responsibilities under the EU Artificial Intelligence Act. It focuses on organizational leadership and regulatory compliance, the need for proactive governance to prevent serious incidents arising from resource inadequacies or oversight gaps. Participants gain functional fluency to authorize controls, challenge risks, and ensure the presumption of conformity. The content is aimed at AI quality management system managers, positioning the quality management system at the centre of regulatory compliance.
Learning Objectives
Upon completion, participants will be able to:
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Understand the QMS Manager's strategic role in tailoring quality systems for high-risk AI under the EU AI Act and harmonized standards like prEN 18286 and ISO/IEC 42001. |
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Recognize the integration of QMS with adjacent regulations such as GDPR, NIS2, and DORA to ensure data integrity, cybersecurity, and resilience. |
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Identify risks like model drift, bias amplification, and unintegrated processes, along with controls to maintain presumption of conformity. |
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Comprehend key deliverables, metrics, and ecosystem components for auditable, proportionate AI governance. |
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Appreciate the manager's accountability, relationships, and integration strategies for fostering a culture of continuous compliance and improvement. |
Target Audience
Senior management (CEOs, CTOs, board members) and GRC team members overseeing high-risk AI systems. Strategic oversight experience assumed.
Duration and Format
- Total Duration: 2.5 hours (self-paced across 4 modules).
- Format: E-learning with videos, readings, quiz, and accessible on-demand.
Prerequisites
Pre-reading: EU AI Act overview (Articles 6-29).
Course Modules
Module 1: Enable effective, compliant, and proportionate governance (30 minutes)
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The QMS Manager enables proportionate QMS implementation, tailoring rigor to organizational scale and AI risks while maintaining non-negotiable safety standards. |
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Governance integrates EU AI Act requirements with GDPR for data protection and NIS2 for cybersecurity resilience in high-risk systems. |
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Effective controls address unintegrated risk management and process inefficiencies to prevent bias amplification and compliance failures. |
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Proportionate measures ensure QMS aligns with DORA for operational resilience and the Product Liability Directive for defect accountability. |
Module 2: Key deliverables, result areas, performance metrics (30 minutes)
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Deliverables include annual QMS strategies, quarterly performance reports, and decision records for traceability and regulatory defense. |
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Result areas encompass audit readiness, incident response, and continuous improvement to sustain presumption of conformity. |
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Performance metrics include 100% audit compliance, zero high-severity nonconformities, and mean time to nonconformity resolution under 10 days. |
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Metrics also track 100% coverage of high-risk systems under monitoring and zero unreported incidents impacting rights or safety. |
Module 3: Knowledge on AI Ecosystem Governance Components (30 minutes)
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Ecosystem components include policy-as-code tools, lineage capture systems, and observability stacks for real-time monitoring. |
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Knowledge covers risks like model drift, bias detection, and supplier chain vulnerabilities with controls for robustness testing. |
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Components involve harmonized standards integration, data governance protocols, and incident response mechanisms. |
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Governance requires understanding of lifecycle phases, from risk assessments to post-market surveillance and revalidation triggers. |
Module 4: Accountability, Relationships, and Integration (30 minutes)
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Accountability includes veto rights over nonconformities and sign-off on conformity assessments for high-risk systems. |
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Relationships involve collaboration with notified bodies, suppliers, and authorities for audits and incident reporting. |
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Integration embeds QMS into MLOps pipelines, training sessions, and stakeholder consultations for cultural alignment. |
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The manager fosters awareness through authoritative communications and escalations to senior governance for critical issues. |
Assessment and Certification
- Assessments: End-of-course quiz (70% pass).
- Certification: "AI quality management system manager AI Literacy Proficiency" certificate.
Resources and Follow-Up
- Follow-Up: Optional 30-minute coaching.
Designed for AI Quality Management System Managers, this course develops the specialized AI literacy required to support providers and deployers in fulfilling Article 4 obligations under the EU AI Act. Participants will acquire the skills, knowledge, and understanding needed to facilitate informed deployment of high-risk AI systems, promote awareness of associated opportunities, risks, and harms, and maintain presumption of conformity through robust, proportionate quality management practices.
