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

The EU AI Act outlines what must be included intechnical documentationfor high-risk systems. These requirements are designed to supportconformity assessment, transparency, and traceability.

From theAI Governance in Practice Report 2025:

“It mandates drawing up technical documentation… must include a general description of the AI system, the intended purpose, and a detailed description of the elements and development process.” (p. 34)

“Documentation… includes training, testing, evaluation procedures, andappropriateness of performance metrics.” (p. 34–35)

Therisk management systemis addressed separately through arisk management plan, not within the technical documentation itself.

Thus:

A, C, and Dare explicitly required in thetechnical documentation.

B, while important, is part of therisk management process, not a required section oftechnical documentation.

The correct answer is A because the first step in any AI project lifecycle is to clearly define the business objective and justify the use of AI. AI governance frameworks emphasize that planning begins with understanding the purpose, value, and necessity of the system before moving into design or risk assessment stages. Establishing a business case ensures alignment with organizational goals, identifies expected benefits, and evaluates whether AI is the appropriate solution. This step corresponds to the planning phase of the AI lifecycle, where objectives and intended outcomes are documented. In contrast, TEVV processes occur during development and testing, while impact assessments and redress consid erations arise later as part of risk management and governance. Starting with a clear “why AI” foundation ensures responsible, efficient, and goal-oriented system development.

The correct answer is A because it directly demonstrates meaningful human oversight over AI-generated outcomes, which is a key requirement in AI governance frameworks and regulations such as the EU AI Act. Human oversight requires that a qualified human can review, intervene, and override AI decisions before they produce legal or significant real-world effects. In high-risk contexts like healthcare diagnostics, governance frameworks emphasize “human-in-the-loop” controls to prevent harm and ensure accountability. Option A ensures a licensed medical professional validates the AI output before it is finalized, aligning with safety, accountability, and risk mitigation principles. Other options describe training, system design, or monitoring, which are important governance measures but do not constitute direct oversight of individual AI decisions at the point of impact, making them insufficient under strict regulatory expectations.

The core ethical concern when deploying diagnostic AI in a healthcare setting is ensuringfairness and accuracy across diverse patient populations. If the AI is trained on a dataset that isnot representativeof the population it will serve, it risks reinforcing health disparities and leading to misdiagnoses.

From theAI Governance in Practice Report 2025:

“Training datasets lacking in diversity can produce outputs that systematically underperform for certain groups… this can lead to inaccurate or biased outcomes in healthcare settings.” (p. 41)

“Bias, discrimination and fairness challenge… inadequate or nonrepresentative training data can result in AI systems that propagate historical disparities.” (p. 42)

While physician oversight may reduce risk,biased data can still shape clinical decision-making.

A– Economic benefit is not central to ethical risk here.

C– Important but less critical than data representativeness.

D– Error rate matters but is addressed via validation; it’s not the core ethical issue.

Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems — regardless of origin — require evaluation for fairness, accuracy, and representativeness.

From theAI Governance in Practice Report 2025:

“Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced.” (p. 41)

External communication plans primarily establish how an organization communicates relevant AI information, risks, incidents, limitations, deployment changes, and governance matters to people outside the organization. A therefore provides the broadest and most accurate purpose. The current IAPP AIGP Study Guide expressly treats external communications as part of ongoing AI operations and associates them with transparency and trust-building. NIST AI RMF likewise requires communication plans for identified and measured AI risks and encourages communication with external stakeholders, affected communities, users, and other relevant AI actors. B concerns internal training rather than external communication. C belongs to third-party procurement and contract governance. Regulatory notification in D may form one component of an external communication plan when legally required, but it is considerably narrower than the overall governance objective of transparent stakeholder communication represented by A.

Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.

From theAI Governance in Practice Report 2025:

“Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms.” (p. 11, 34)

Ais about liability, not auditability.

Bmatters for IP rights, not oversight.

Crelates to lifecycle responsibility but doesn’t guarantee audit access.

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 2025:

“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.

In the selection and implementation of the AI hiring tool, involving Finance and Legal is crucial. The Finance team is essential for assessing cost implications, budget considerations, and financial risks. The Legal team is necessary to ensure compliance with applicable laws and regulations, including those related to data privacy, employment, and anti-discrimination. Involving these stakeholders ensures a comprehensive evaluation of both the financial viability and legal compliance of the AI tool, mitigating potential risks and aligning with organizational objectives and regulatory requirements.

The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework. The focus is more on the system ' s ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.