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

Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.

From theAI Governance in Practice Report 2024:

“AI governance starts with defining the corporate strategy for AI… and aligning systems with business purpose and operational context.” (p. 11)

B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.

Under theEU AI Act, serious incidents involvinghigh-risk AI systemsmust be reported. The deployer is required topromptly inform the provider and relevant authoritiesabout the issue.

From theAI Governance in Practice Report 2024:

“Serious incidents involving high-risk systems… must be reported to the provider and relevant market surveillance authority.” (p. 35)

“Timely reporting is required when AI systems result in or may result in violations of fundamental rights.” (p. 35)

Offeringloan products based on current offerings and rulesrequires a system that can followexplicit business logic, not generate open-ended content. Anexpert system, which is a rules-based AI that uses “if-then” logic, is ideal here.

From the AI governance context:

“Rule-based AI systems are often preferred when decisions must adhere to precise regulatory or financial criteria.” (aligned with AI best practices in regulated sectors)

A. RAGis used to integrate external knowledge—not suitable for structured, rule-based logic.

B. Multimodal modelshandle varied input types—not needed here.

D. Quantum computingis not yet practical or relevant for this business use case.

When notifying an accused perpetrator, the police officer should provide information about how the individual was identified by the AI system. This transparency is crucial for maintaining trust and ensuring that the accused understands the basis of the charges against them. Information about the accuracy, how to oppose the charges, and the composition of the training data, while potentially relevant, do not directly address the immediate need for the accused to understand the specific process that led to their identification. Reference: AIGP Body of Knowledge on AI Transparency and Explainability.

Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.

Once a company moves from being adeployerto also acting as aprovider or developer, it assumesnew obligationsunder regulations like the EU AI Act. One of the core requirements for providers is to produce and maintaintechnical documentation, including descriptions of the model, associated risks, and mitigation strategies.

From theAI Governance in Practice Report 2024:

“Providers of high-risk AI systems must draw up technical documentation demonstrating the system’s conformity with the requirements... including potential risks and safeguards applied.” (p. 34)

“This documentation must be available before placing the system on the market.” (p. 35)

Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.

The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals. Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

Conformity assessmentsare a core requirement under theEU AI Actfor high-risk systems and serve to confirm that the AI meetsregulatory, safety, and ethical standardsbefore it is put into production.

From theAI Governance in Practice Report 2024:

“Conformity assessments… ensure that systems comply with legal requirements, safety criteria, and intended purpose before being placed on the market.” (p. 34)

“They are a critical step to demonstrate safety and trustworthiness in AI deployment.” (p. 35)