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

An AI system’sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.

From theAI Governance in Practice Report 2024:

“There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization’s role, footprint, broader risk-governance profile and maturity.” (p. 4)

“AI governance starts with defining the corporate strategy for AI… and formulating policy standards and operational procedures to reflect industry, use case, and location.” (p. 11)

A– Organizational accountability is broader and not directly scoped by industry or function.

C– Diversity of data sources is tied to data strategy.

D– Explainability is more influenced by model type, not use context.

Utilizing synthetic data to offset the lack of patient data is an efficient solution that balances privacy concerns with the need for sufficient data to train the model. Synthetic data can be generated to simulate real patient data while avoiding the privacy issues associated with using actual patient data. This approach allows for the development and testing of the AI algorithm without compromising patient privacy, and it can be refined with real data as it becomes available. Reference: AIGP Body of Knowledge on Data Privacy and AI Model Training.

Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline—allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.

Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high-stakes environmentswhere hallucinations are a concern.

B, C, and Ddescribe optimization or bias mitigation—not the core function of RAG.

The most significant risk from combining the healthcare network’s existing data with the clinical research partner data is privacy risk. Combining data sets, especially in healthcare, often involves handling sensitive information that could lead to privacy breaches if not managed properly. De-identified data can still pose re-identification risks when combined with other data sets. Ensuring privacy involves implementing robust data protection measures, maintaining compliance with privacy regulations such as HIPAA, and conducting thorough privacy impact assessments. Reference: AIGP Body of Knowledge on Data Privacy and Security.

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 Report 2024:

“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.

Third-party risk management for AI systems should beproportional and risk-based, involvinginitial due diligenceandongoing monitoringthat reflects thelevel of risk posedby the third party's AI system.

From theAI Governance in Practice Report 2024:

“Third-party due diligence assessments to identify possible external risk and inform selection.” (p. 11)

“Legal due diligence may include verification of the personal data's lawful collection by the data broker, review of contractual obligations…” (p. 19)

Afocuses too narrowly on financial stability.

Cis excessive and not scalable or aligned with best practices.

Dinappropriately separates ethical and technical risks; both must be evaluated holistically.

To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.

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.

After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.