IAPP Certified Artificial Intelligence Governance Professional (AIGP) Exam Syllabus

• Technology (engineered or machine-based system; or logic, knowledge, or learning algorithm).
• Automation (elements of varying levels).
• Role of humans (define objectives or provide data).
• Output (content, predictions, recommendations, or decisions).

- Understand what it means that an AI system is a socio-technical system.
- Understand the need for cross-disciplinary collaboration (ensure UX, anthropology, sociology, linguistics experts are involved and valued).
- Knowledge of the OECD framework for the classification of AI systems.
- Understand the use cases and benefits of AI (recognition, event detection, forecasting, personalization, interaction support, goal-driven optimization, recommendation).

• Data gathering, wrangling, cleansing, labeling.
• Applying PETs like anonymization, minimization, differential privacy, federated learning.

- Determine AI system architecture and model selection (choose the algorithm according to the desired level of accuracy and interpretability).

• Understanding pressures on tech teams to build AI solutions quickly and efficiently.
• Understanding how data science and model operations teams work.
• Being able to influence behavioral and cultural change.

- Establish organizational risk strategy and tolerance.
- Develop central inventory of AI and ML applications and repository of algorithms.
- Develop responsible AI accountability policies and incentive structures.
- Understand AI regulatory requirements.
- Set common AI terms and taxonomy for the organization.
- Provide knowledge resources and training to the enterprise to foster a culture that continuously promotes ethical behavior.
- Determine AI maturity levels of business functions and address insufficiencies.
- Use and adapt existing privacy and data governance practices for AI management.
- Create policies to manage third party risk, to ensure end-to-end accountability.
- Understand differences in norms/expectations across countries.

• Evaluate stakeholder salience.
• Include diversity of demographics, disciplines, experience, expertise and backgrounds.
• Perform positionality exercise.
• Determine level of engagement.
• Establish engagement methods.
• Identify AI actors during design, development, and deployment phases.
• Create communication plans for regulators and consumers that reflect compliance/disclosure obligations for transparency and explainability (UI copy, FAQs, online documentation, model or system cards).

- Determine feasibility of optionality and redress.
- Chart data lineage and provenance, ensuring data is representative, accurate and unbiased. Use statistical sampling to identify data gaps.
- Solicit early and continuous feedback from those who may be most impacted by AI systems.
- Use test, evaluation, verification, validation (TEVV) process.
- Create preliminary analysis report on risk factor and proportionate management.

• Use edge cases, unseen data, or potential malicious input to test the AI models.
• Conduct repeatability assessments.
• Complete model cards/fact sheets.
• Create counterfactual explanations (CFEs).
• Conduct adversarial testing and threat modeling to identify security threats.
• Refer to OECD catalogue of tools and metrics for trustworthy AI.
• Establish multiple layers of mitigation to stop system errors or failures at different levels or modules of the AI system.
• Understand trade-offs among mitigation strategies.

- Apply key concepts of privacy-preserving machine learning and use privacy-enhancing technologies and privacy-preserving machine learning techniques to help with privacy protection in AI/ML systems.
- Understand why AI systems fail. Examples include: brittleness;hallucinations; embedded bias; catastrophic forgetting; uncertainty; false positives.
- Determine degree of remediability of adverse impacts.
- Conduct risk tracking to document how risks may change over time.
- Consider, and select among different deployment strategies.