IAPP AIGP - Artificial Intelligence Governance Professional

AI governance frameworks consistently define the“unique characteristics”of AI that create novel governance challenges.

Across the OECD AI Principles, NIST AI RMF, ISO/IEC 42001, and the EU AI Act, the key characteristics requiring governance are:

Autonomy– AI systems act without direct human intervention and take context-dependent decisions.

Adaptability– AI systems learn, evolve, and update their behavior over time, making outputs non-deterministic.

These traits fundamentally distinguish AI from traditional software and shape all major governance activities (risk assessment, monitoring, assurance, accountability, alignment, etc.).

Below is why each option is correct or incorrect:

A. Autonomy — NOT selected

Reason:

Autonomy is repeatedly cited as one of the defining characteristics that necessitates governance.

NIST AI RMF references the risks ofautonomous behavior.

ISO/IEC 42001 emphasizes governance controls for “systems operating with varying levels of autonomy.”

Thus,Autonomy IS a unique characteristic, so it isnotan answer.

B. Automation — SELECTED

Reason:

“Automation” is not a unique AI property. Automation predates AI and applies to robotics, scripts, business process automation, etc.

AI governance literature doesnotclassify automation as a unique AI-specific characteristic.

Automation is aresultof AI, but not adistinguishing property.

Therefore,Automation is correctly selected as NOT a unique AI characteristic.

C. Adaptability — NOT selected

Reason:

Adaptability (systems that update, learn, or change behavior) is recognized universally as a core distinctive trait of AI.

EU AI Act emphasizes governance requirements for “adaptive systems.”

NIST AI RMF highlights “learning and emergent behavior.”

Thus,Adaptability IS a unique characteristic, so it shouldnotbe chosen as an exception.

D. Speed and scale — SELECTED

Reason:

While AI can operate at high speed and scale,these are not unique characteristics of AI.

Traditional computing systems, cloud workloads, and automation pipelines operate at similar scale.

AI governance documents mention speed/scale as arisk amplifier, not a defining characteristic.

Therefore,Speed and scaleshould be selected.

E. Superintelligence — SELECTED

Reason:

“Superintelligence” is not a characteristic of current AI systems and isnot referenced as a governance-relevant attributein mainstream policy documents (OECD, NIST, ISO, EU AI Act).

It is speculative rather than a defining feature.

Therefore, it is correctly chosen as NOT a unique characteristic requiring governance today.

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.

Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization. Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning​​.

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).

Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive abilities. For the self-driving car to be classified as "strong" AI, it would need to possess full human cognitive abilities to make independent decisions beyond pre-programmed instructions. Reference: AIGP BODY OF KNOWLEDGE and AI classifications.

The GDPR's transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.

The correct answer isA. While TEVV is important inlater lifecycle phases, it isnot the primary considerationwhen evaluating and prioritizinguse cases.

From the AIGP Body of Knowledge – Use Case Assessment Module:

“Use case evaluation focuses on value, impact, fairness, and accessibility—technical testing considerations come later.”

ILT Guide confirms:

“Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization.”

Thus,Ais not a top-level consideration during use caseselection.

The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation. A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €15 million or 3% of annual global turnover.

From the AIGP ILT Guide – EU AI Act Module:

“Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover.”

AI Governance in Practice Report2025supports this:

“Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article 71(3) of the EU AI Act.”

Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.

===========

The correct answer isD. Transparency obligations under data protection laws, such as GDPR and most AI governance frameworks, require thatusers whose data is being processedbe directly informed.

From the AIGP ILT Guide (Privacy Module):

“The user privacy notice must be updated to explain the nature of automated processing, the logic involved, and the significance and consequences for the data subject.”

Also, per AI Governance in Practice Report2025(Part III):

“Transparency obligations apply throughout the lifecycle of AI… Individuals must be informed about automated decision-making and profiling that may impact them.”

Unlike internal policies or general privacy notices,the user privacy noticeprovides direct transparency to theindividual data subjectsaffected by AI processing.

===========