Pass the IAPP Artificial Intelligence Governance AIGP Questions and answers with CertsForce

The GDPR requires that individuals have the right to not be subject to decisions based solely on automated processing, including profiling, unless specific exceptions apply. One effective control is to establish a human-in-the-loop procedure (D), ensuring human oversight and the ability to contest decisions. This goes beyond just-in-time notices (A), data safeguarding (B), or review rights (C), providing a more robust mechanism to protect individuals ' rights.

The primary reason the EU is considering updates to its Product Liability Directive is to address digital services and connected products. The current directive does not adequately cover the complexities and challenges posed by modern digital and connected technologies. By updating the directive, the EU aims to ensure that it remains relevant and effective in addressing the liabilities associated with these advanced products, ensuring consumer protection and fair market practices in the digital age.

In the context of AI, particularly generative models, " hallucination " refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.

Third-party AI governance introduces issues that arise specifically because an organization depends on an external model provider. These include limited visibility into model architecture, training sources, capabilities and limitations; cybersecurity and information-security risks created through external integrations; and determining contractual responsibility and liability between the organization and vendor. These considerations make A, C, and D directly relevant to third-party governance. Back testing, by contrast, is primarily a model testing or validation methodology rather than a compliance consideration specific to the third-party relationship. Testing third-party AI systems remains an important governance practice, but the term " back testing " itself describes performance evaluation rather than vendor governance. NIST guidance emphasizes third-party transparency, due diligence, security, procurement controls, contractual expectations, and continuing risk management when organizations acquire or integrate external AI systems.

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The correct answer isB – Procedural. TheOECD Framework for Classifying AI Systemscategorizescodes of conduct and collective agreementsasprocedural toolsbecause they guide internal governance and decision-making processes.

From the AIGP ILT Participant Guide – Global Governance Models:

“Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement.”

AI Governance in Practice Report 2025 elaborates:

“These procedural tools support internal accountability mechanisms and ethics compliance frameworks… they are part of soft governance.”

These toolsdo not measure or analyze technical performance, hence they arenot technical or analytic.

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To mitigate the risk of reputational harm from using an AI hiring tool, XYZ Corp should rigorously test the AI tool both before and after deployment. Pre-deployment testing ensures the tool works correctly and does not introduce bias or other issues. Post-deployment testing ensures the tool continues to operate as intended and adapts to any changes in data or usage patterns. This approach helps to identify and address potential issues proactively, thereby reducing the risk of reputational harm. Ensuring the vendor assumes responsibility for damages (B) does not address the root cause of potential issues, selecting the most economical tool (C) may compromise quality, and continuing manual screening (D) defeats the purpose of using the AI tool​​​​.

AI ' s resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model ' s predictions. Model drift is when the model ' s performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

The best reason for GVC to offer students the choice to utilize generative AI in limited, defined circumstances is to enable students to learn how to use AI as a supportive educational tool. By integrating AI in a controlled manner, students can learn the practical applications of AI and develop skills to use AI responsibly and effectively in their educational pursuits.

Deployers of proprietary models arenot responsible for design, but they are accountable for how the system performsin their context of use, including ensuring ethical behavior, performance, and legal compliance.

From theAI Governance in Practice Report 2025:

“Deployers of AI systems must take reasonable steps to ensure that systems are used ethically, perform safely, and align with applicable laws and standards.” (p. 11–12)

“Operational governance… includes performance monitoring protocols, incident management plans, and regulatory oversight.” (p. 12)

Thus:

✅A. Ethical testing– Required to mitigate misuse and unintended harms.

❌B. Ethical design– Belongs todevelopers/providers, not deployers.

✅C. Technical performance– Deployers must ensure that AI performs as expected.

❌D. System documentation– This is theprovider’sobligation.

✅E. Regulatory compliance– Deployers must ensure system use complies with applicable laws.