Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.

The correct answer is Explainable AI because it specifically refers to the ability of a system to describe the logic behind its decisions or outputs in a way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressing the black-box problem in AI.

From the AIGP ILT Participant Guide (Module on Transparency and Explainability):

“Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems.”

Also, according to the AI Governance in Practice Report 2024:

“Explainability refers to the representation of the underlying mechanisms of the AI system’s operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained.”

Thus, while Trustworthy and Responsible AI are broader concepts, explainability specifically targets the regulatory concern about understanding outputs.

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The correct answer is A. Only GPAI models with systemic risk must publish a detailed summary of training data to meet transparency and accountability standards under the EU AI Act.

From the AI Governance in Practice Report 2024 (EU AI Act Section):

“For GPAI systems with systemic risk, providers must publish sufficiently detailed summaries of the content used to train the model.”

Also, the AIGP ILT Guide confirms:

“The obligation to disclose summaries of training data applies only to systemic-risk GPAI models, not all general-purpose models or high-risk systems.”

This unique requirement is part of the Act’s effort to increase transparency and auditability for powerful foundational models.

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Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

The correct answer is A. While educating technical staff is important, expecting all technical employees to be retooled as AI developers is unrealistic and not aligned with scalable governance practices.

From the AIGP ILT Guide:

"Training approaches should be role-specific and align with the individual's function and responsibilities... Organizations typically do not expect every technical role to participate in model development."

The AI Governance in Practice Report 2024 supports tailored approaches:

“Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension.”

Thus, broad development training for all technical employees is the least practical and least likely approach.

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The correct answer is B. "Candidate quality" is not a negative consequence of using AI—rather, it is the intended benefit of using such tools (e.g., more efficient filtering of strong candidates).

From the AIGP ILT Guide:

“Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring. These risks may arise when AI models replicate biases present in training data or obscure the decision logic.”

AI Governance in Practice Report 2024 (Bias and Fairness Section) also warns:

“Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups.”

Candidate quality is a goal, not a risk, making B the correct answer for what is not a negative outcome.

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The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).