AIGP Free Study Guide
- Olufunmilayo Owolabi
- 2 days ago
- 2 min read
Prepared by Privacy Professional Training LLCAIGP Free Study Guide
Introduction to Artificial Intelligence Governance & the AIGP Exam
The Artificial Intelligence Governance Professional (AIGP) certification from the IAPP validates your ability to navigate and apply responsible AI governance frameworks. As AI rapidly transforms industries, the AIGP equips professionals to manage ethical, legal, and technical risks associated with AI systems.
Why Pursue AIGP?
Demonstrates expertise in AI risk management and compliance
Aligns with global trends in responsible AI development
Supports organizations in building trust in AI technologies
Exam Overview
100 multiple-choice questions
3-hour time limit
Delivered via Pearson VUE test centers or online
Core Exam Domains:
Foundations of Artificial Intelligence
Responsible AI Principles and Impacts
AI Governance and Risk Management
Legal Frameworks and Emerging Laws
Implementing AI Governance in Practice
Understanding the Foundations of AI
What Is AI?
AI refers to systems designed to mimic human intelligence, including learning, reasoning, and problem-solving
Common subsets include machine learning (ML), natural language processing (NLP), and computer vision
The AI Technology Stack
Data → Algorithms → Models → Outputs
Importance of data quality and transparency at every layer
AI Use Cases
Finance, healthcare, retail, government, and more
From chatbots and fraud detection to predictive maintenance and medical diagnosis
AI vs. Traditional Software
AI systems learn from data and adapt over time
AI introduces uncertainty and requires additional governance compared to static rule-based systems
Responsible AI Principles, Risk, and Regulation
Responsible AI Principles
Fairness: Avoid bias and ensure equitable outcomes
Transparency: Explainable processes and decisions
Accountability: Clear ownership and auditability
Safety & Security: Robustness against harm or misuse
Risks of AI Deployment
Data quality issues
Bias and discrimination
Lack of explainability
Security vulnerabilities
Emerging Legal and Regulatory Landscape
EU AI Act (proposed): Risk-based regulatory approach to AI use
U.S. NIST AI Risk Management Framework
OECD and UNESCO AI principles
Increasing global demand for ethical, transparent AI systems
Implementing AI Governance in Practice
Building AI Governance Programs
Define organizational AI policies and values
Aith applicable laws and standards
Assign roles and responsibilities for oversight
AI Risk Assessments
Identify system objectives and potential impacts
Evaluate model performance, explainability, and fairness
Document mitigation strategies
Data Lifecycle & Governance
Data sourcing, consent, labeling, storage, and deletion
Transparency in data lineage and documentation
Ongoing Monitoring and Compliance
Use model cards, data sheets, and risk logs
Periodic review of outcomes and risks
Establish feedback loops for continuous improvement
Study Approach & Sample Practice Questions
Study Tips
Understand the AIGP exam domains as outlined by the IAPP
Learn how AI systems function technically and legally
Study responsible AI principles and how to apply them
Focus on frameworks like the EU AI Act and NIST AI RMF
Privacy Professional Training LLC We offer specialized training for AIGP preparation, including:
Self-paced modules covering all AIGP domains
Downloadable worksheets and glossaries
Focused learning paths that align with official IAPP materials
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