IAPP Updated AIGP Exam Questions and Answers by ray

Ensuring fairness when training an AI system largely depends on the data attributes and variability. This involves having a diverse and representative dataset that accurately reflects the population the AI system will serve. Fairness can be compromised if the data is biased or lacks variability, as the model may learn and perpetuate these biases. Diverse data attributes ensure that the model learns from a wide range of examples, reducing the risk of biased predictions. Reference: AIGP Body of Knowledge on Ethical AI Principles and Data Management.

The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.

From theAI Governance in Practice Report2025:

“It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations.” (p. 34)

“To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle.”

“Conducting post-market monitoring” and “conformity assessments” areexplicitrequirements for high-risk systems. (p. 34–35)

However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.

A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.

TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systemsimpact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or usedentirely outside of the EU—such as in theUK, unless they affect the EU market or individuals.

From theAI Governance in Practice Report2025:

“The act imposes regulatory obligations… depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems.” (p. 7)

“The EU AI Act is the world’s first comprehensive AI regulation… requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market.” (p. 7–8)

Thus:

A. Open source releasedoes not exclude applicability if deployed in the EU.

B. Deployment at an EU borderclearly invokes jurisdiction.

D. Training by an EU companycreates jurisdictional links.

C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.

Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in,classic ML models(e.g., regression, decision trees, random forests) are optimal.

From theAI Governance in Practice Report2025:

“Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance.” (p. 33)

AandCmay have limited transparency and are often tied to specific providers.

Dinvolves ongoing costs and limited model control.