Certified Artificial Intelligence Compliance Professional (CAICP)
4 DaysCertified Artificial Intelligence Compliance Professional (CAICP)
Course Methodology
This is an interactive course that utilizes group discussions, team-based practical exercises, presentations and mock exams.
Course Objectives
By the end of the course, participants will be able to:
- Describe how AI systems can be categorized based on their risk levels and outline the implications of each category
- Apply data protection principles to assess the privacy impact of an AI system in a given scenario
- Analyze the trade-offs between innovation and regulation when developing or deploying AI technologies
- Evaluate the ethical considerations of using AI in high-stakes sectors such as healthcare, justice, or education
- Compare various European and international standards (e.g., ISO/IEC 42001, CEN/CLC/TR 18115, NIST) that guide responsible and compliant AI development and usage
Target Audience
The target group for the EXIN Artificial Intelligence Compliance Professional certification includes professionals responsible for ensuring adherence to AI regulations. This includes, but is not limited to; compliance, risk, legal, data protection, AI development, IT Security and C-Suite professionals.
Target Competencies
- Compliance Management
- Risk Management
- Legal Compliance
- AI Development
- Regulation Compliance
Course Outline
- Context of the Artificial Intelligence (AI) Act
- Purpose and Scope
- Overview of the objectives behind the AI Act
- Scope and applicability of the regulation
- Roles and responsibilities defined under the Act
- Purpose and Scope
- AI Act in Depth
- Key Provisions
- Classification of AI systems by risk level: unacceptable, high, limited, minimal
- Requirements and obligations for high-risk AI systems
- Governance structures and regulatory oversight
- Key Provisions
- Risks and Regulations
- Balancing innovation with regulation
- General-purpose AI, systemic risks, and codes of practice
- Intellectual property considerations in the AI landscape
- Open-source vs. closed-source models: regulatory implications
- Obligations for AI providers, including assessments and documentation
- Compliance and Enforcement
- Accountability mechanisms and compliance structures
- Incident reporting procedures and response frameworks
- Role of whistleblowers in AI governance
- Penalties and consequences for non-compliance
- User responsibilities for safe and compliant AI usage
- Trustworthy AI
- Privacy and Data Protection
- The role of data management and privacy in AI
- Applying GDPR principles to AI scenarios
- Data minimization and its impact on AI systems
- Transparency and Traceability
- Importance of transparency in AI development and deployment
- Public trust and the role of explainable AI
- Mechanisms to ensure traceability in AI decision-making
- Privacy and Data Protection
- Ethical AI
- Principles and Guidelines
- Ethical foundations in AI development
- Practical application of ethical guidelines in real-world scenarios
- Principles and Guidelines
- Human Rights
- Ensuring human oversight in AI processes
- Understanding the rights of individuals affected by AI systems
- AI Act in Practice
- AI in the Public Sector
- Risks and challenges of deploying AI in public decision-making, law enforcement, and elections
- AI in the Public Sector
- AI in the Private Sector
- Sector-specific impacts of AI regulation:
- Finance and insurance
- Healthcare
- Employment and education
- Autonomous vehicles
- Advertising and tourism
- Sector-specific impacts of AI regulation:
- Frameworks to Support Compliance
- European Standards
- Role of CEN/CLC/TR 18115 in supporting compliant data governance
- International Standards
- Overview of ISO/IEC 42001 and the NIST framework for responsible AI
- ISO/IEC 23894 for AI risk management practices
- Ethical considerations in ISO/IEC TR 24368
- European Standards
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