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  1. Home
  2. IAPP Certification
  3. AIGP Exam
  4. IAPP.AIGP.v2026-11-12.q219 Dumps
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Question 51

A US hospital plans to develop an AI that will review available patient data in order to propose an initial diagnosis to licensed physicians. The hospital will implement a policy that requires physicians to consider the AI proposal, but conduct their own physical examinations prior to making a final diagnosis.
An important ethical concern with this plan is?

Correct Answer: B
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 Report2025:
"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.
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Question 52

Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

Correct Answer: A
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
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Question 53

Which of the following is NOT required to be included in an AI impact assessment for a narrow AI use case?

Correct Answer: D
The correct answer is D because AI impact assessments primarily focus on risks to individuals, society, and legal compliance, such as privacy, fairness, and potential harm. Core elements typically include evaluating the impact on privacy rights, identifying data sources and their lawfulness, and assessing potential bias or discriminatory outcomes. These factors are directly tied to regulatory expectations and ethical AI governance principles. While environmental considerations like energy consumption are increasingly discussed in broader AI sustainability conversations, they are not standard or required components of most AI impact assessments, especially for narrow use cases. AI governance frameworks prioritize human-centric risks, ensuring systems are lawful, fair, and safe, rather than focusing on operational metrics like energy usage in typical assessment processes.
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Question 54

All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?

Correct Answer: C
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.
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Question 55

All of the following areunique characteristics of AIthat require a comprehensive approach to governanceEXCEPT?

Correct Answer: E
The correct answer isE - Superintelligence.
While the other options (Autonomy, Automation, Adaptability, Speed and Scale) are commonly cited as real- world characteristics that affect governance today,superintelligenceremainsa theoretical concept.
From the AIGP ILT Guide and AI Governance in Practice Report2025:
"Core AI characteristics-such as automation, adaptability, speed, and autonomy-require active governance due to their impact on decision-making, legal liability, and risk." However,superintelligenceis speculative and not a current feature of AI systems under governance frameworks like theEU AI Act or NIST RMF.
Thus, it'snot a current characteristicrequiring governance in real-world enterprise settings.
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