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| Topic |
Details |
| Topic 1 |
- Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.
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| Topic 2 |
- Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.
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| Topic 3 |
- Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.
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| Topic 4 |
- Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
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| Topic 5 |
- Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
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| Topic 6 |
- Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q16-Q21):
NEW QUESTION # 16
All of the following are elements of establishing a global Al governance infrastructure EXCEPT?
- A. Providing training to foster a culture that promotes ethical behavior.
- B. Understanding differences in norms across countries.
- C. Publicly disclosing ethical principles.
- D. Creating policies and procedures to manage third-partyrisk.
Answer: C
Explanation:
Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference:
AIGP Body of Knowledge on AI Governance and Infrastructure.
NEW QUESTION # 17
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?
- A. Procure more data from clinical research partners.
- B. Create a bias bounty program.
- C. Perform an impact assessment.
- D. Engage a third party to perform an audit.
Answer: C
Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.
NEW QUESTION # 18
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?
- A. GPUs run many tasks concurrently, resulting in faster processing.
- B. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
- C. GPUs can run every task on a computer, making them more robust than CPUs.
- D. GPUs can access memory quickly, resulting in lower latency than CPUs.
Answer: A
Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference: AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.
NEW QUESTION # 19
A company initially intended to use a large data set containing personal information to train an Al model.
After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?
- A. Federated learning.
- B. Differential privacy.
- C. Anonymization.
- D. Pseudonymization.
Answer: C
Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.
NEW QUESTION # 20
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
- A. Determining the level of human involvement in algorithmic decision-making.
- B. Adapting the existing governance structure algorithmic decision-making.
- C. Establishing communications and collaboration among stakeholders.
- D. Employing human-over-the-loop protocols for high-risk systems.
Answer: D
Explanation:
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.
NEW QUESTION # 21
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