Training Course on Artificial Intelligence (AI) Regulations-EU AI Act

Training Course on Artificial Intelligence (AI) Regulations-EU AI Act

Introduction

Artificial Intelligence (AI) is revolutionizing industries worldwide, but its growth demands responsible governance and legal oversight. The EU Artificial Intelligence Act (EU AI Act) stands as the first comprehensive regulatory framework designed to manage AI risks and ensure safe deployment within the European Union. Training Course on Artificial Intelligence (AI) Regulations – EU AI Act provides a future-ready, compliance-driven, and ethically aligned curriculum to help professionals, regulators, and developers adapt to evolving standards. It emphasizes risk-based classification, AI transparency, data governance, and user protection.

 

In an era marked by AI-powered innovations, understanding legal, ethical, and compliance dimensions is critical for sustainable growth. With GDPR synergy, automated decision-making standards, and AI accountability, this course offers practical insights into implementing compliant AI systems. Participants will gain real-world knowledge through case studies, regulatory breakdowns, and strategic risk assessments. Whether you are a policymaker, engineer, or executive, this course ensures you stay compliant, competitive, and responsible.

Course Objectives

1. Understand the core principles and structure of the EU AI Act.
2. Identify AI risk categories: unacceptable, high-risk, limited-risk, and minimal-risk systems.
3. Evaluate data governance and algorithmic accountability in high-risk AI systems.
4. Examine transparency and explainability mandates for AI developers.
5. Understand GDPR implications and interoperability with the EU AI Act.
6. Learn documentation, record-keeping, and conformity assessment obligations.
7. Implement human oversight mechanisms in AI operations.
8. Assess the role of post-market monitoring and risk management.
9. Compare global AI regulations and the EU’s strategic leadership.
10. Apply ethical AI frameworks in regulatory compliance.
11. Learn the enforcement roles of national competent authorities.
12. Navigate AI innovation within regulatory sandboxes.
13. Conduct compliance audits and technical documentation reviews

Target Audience

1. Legal professionals in tech law
2. Compliance officers in AI-driven industries
3. Government policymakers and regulators
4. AI and ML developers
5. Data protection officers (DPOs)
6. Tech company executives
7. Academic researchers in AI ethics
8. Start-up founders in the AI domain

Course Duration: 10 days

Course Modules

Module 1: Introduction to AI Governance

• Evolution of AI regulation in Europe
• Scope and objectives of the EU AI Act
• Defining AI under the EU framework
• Risk-based classification of AI systems
• Stakeholder responsibilities
• Case Study: Facial recognition bans in public surveillance

Module 2: Understanding Risk Categories in AI

• Unacceptable risk AI systems
• High-risk system identification
• Requirements for limited and minimal risk
• Use-case scenarios
• Penalties and consequences
• Case Study: Real-time biometric surveillance in transportation

Module 3: Data Governance and AI

• Data quality and dataset governance
• Bias prevention and diversity
• Secure data acquisition practices
• Recordkeeping under Article 10
• Human review of data handling
• Case Study: AI in credit scoring systems

Module 4: Transparency and User Rights

• Obligations for transparency
• Notification of AI interaction
• User awareness strategies
• Automated decision-making rules
• Non-discrimination safeguards
• Case Study: Chatbot disclosures in public services

Module 5: Human Oversight in AI Systems

• Role of human-in-the-loop mechanisms
• Prevention of automation bias
• Manual override procedures
• Risk mitigation techniques
• Operator responsibilities
• Case Study: AI in healthcare diagnostics

Module 6: Conformity Assessment Procedures

• CE marking for AI systems
• Third-party audit mechanisms
• Technical documentation essentials
• Compliance verification steps
• Harmonized standards application
• Case Study: CE compliance in robotic process automation

Module 7: Post-Market Monitoring & Risk Management

• Continuous risk assessment
• Monitoring system performance
• Incident reporting under Article 65
• Corrective actions
• Cybersecurity alignment
• Case Study: AI malfunction in autonomous vehicles

Module 8: GDPR and the EU AI Act

• Comparing GDPR and AI Act
• Personal data processing in AI
• Consent under AI operations
• Data minimization principles
• Rights of data subjects
• Case Study: AI hiring tools and data privacy

Module 9: Enforcement Mechanisms

• Role of national supervisory authorities
• Penalties for non-compliance
• Investigation powers
• Prohibition orders
• Redress mechanisms
• Case Study: Enforcement action against an AI startup

Module 10: Regulatory Sandboxes

• Innovation-friendly environments
• Risk-controlled testing
• Sandbox eligibility and criteria
• Benefits for SMEs
• Collaboration with authorities
• Case Study: Testing AI-based mental health apps

Module 11: Global Comparisons in AI Regulation

• U.S. AI policy overview
• China’s AI ethical framework
• OECD AI principles
• Regulatory fragmentation
• Cross-border compliance strategies
• Case Study: Cross-border AI medical diagnostics

Module 12: Ethical Considerations in AI

• Fairness, accountability, and transparency
• Bias reduction strategies
• Societal impact analysis
• Inclusive design practices
• Long-term risk forecasting
• Case Study: Ethical AI deployment in public housing

Module 13: Industry-Specific Implications

• AI in finance and insurance
• AI in transportation and logistics
• Healthcare and medical AI
• Educational technology and AI
• AI in defense and security
• Case Study: Predictive policing tools and risk

Module 14: Technical Documentation and Audit Preparation

• Preparing technical files
• Compliance audit strategies
• Required documentation elements
• Version control and system logs
• Responding to regulator inquiries
• Case Study: Audit readiness in a health AI company

Module 15: Future Trends in AI Regulation

• Evolution of EU AI policy post-2025
• Quantum AI and regulation gaps
• AI and green policy alignment
• Anticipating ethical dilemmas
• Role of civil society in oversight
• Case Study: AI and environmental decision systems

Training Methodology

• Instructor-led virtual or in-person training sessions
• Interactive case study workshops
• AI compliance simulation exercises
• Downloadable toolkits and checklists
• Quizzes and knowledge assessments
• Final capstone project and certification

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.