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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk Frameworks and Standards
|
| Topic 2: AI Governance Management | - Emerging Issues
|
| Topic 3: AI Governance Implementation | - Organizational Governance
|
| Topic 4: Foundations of AI Governance | - AI Governance Fundamentals
|
| Topic 5: AI Laws, Regulations, and Standards | - Regional and National Regulations
|
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35. Frage
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to 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 to a radiologist for secondary review pursuant to agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has:
- Defined its AI ethical principles.
- Conducted discovery to identify the intended uses and success
criteria for the system.
- Established an AI risk committee.
- Assembled a cross-functional team with clear roles and
responsibilities.
- 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. It also intends to retain a large consulting firm to supplement its small data science team and 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.
In the design phase, what is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?
Antwort: D
Begründung:
Identifying fits and gaps in the combined data helps the healthcare network understand the compatibility and completeness of datasets for effective AI model development.
36. Frage
All of the following analyses are part of a deactivating risk strategy EXCEPT?
Antwort: D
Begründung:
The correct answer is C because understanding algorithmic methodology is primarily a design, development, and model evaluation activity rather than part of a deactivation or sunset risk strategy. A deactivating risk strategy focuses on safely retiring or discontinuing an AI system, ensuring that operational, legal, and business risks are properly managed. This includes evaluating regulatory requirements such as data retention and deletion obligations, assessing business continuity and financial implications, and understanding dependencies with upstream and downstream systems to avoid disruption. These steps ensure a controlled and compliant system shutdown. In contrast, analyzing algorithmic methodology is relevant during system development, validation, and explainability efforts, not during the decommissioning phase of the AI lifecycle.
37. Frage
CASE STUDY
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The organization continues planning the adoption of an AI tool to support hiring, but is concerned about potential bias in content generated by AI systems and how that could affect public perception.
Which of the following measures should the company adopt tobest mitigate its risk of reputational harmfrom using the AI tool?
Antwort: C
Begründung:
Note:This is the same scenario and question as Question 21 and thus has thesame correct answer: A. It's possible this was duplicated in your original input.
Repeated for clarity:
"Testing AI tools pre- and post-deployment helps ensure they perform as expected and do not introduce bias, privacy issues, or fairness concerns. This mitigates reputational and legal risk." TheAI Governance in Practice Report2025further reinforces:
"Ongoing monitoring and testing post-deployment allows organizations to catch and correct unintended impacts... especially important in HR and hiring contexts."
38. Frage
During the first month when the company monitors the model for bias, it is most important to?
Antwort: A
Begründung:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking for bias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report 2024:
"Performance monitoring protocols... should include mechanisms to assess and measure disparities in outcomes across different demographic groups." (p. 12)
"Bias may not be evident during pre-deployment testing but can emerge in real-world use." (p. 41)
* B. Awareness trainingis helpful, but not a technical bias mitigation activity.
* C. Analyzing training datais apre-deploymenttask.
* D. Documenting human decisionsmay support auditability but doesn't detect bias in AI outputs.
39. Frage
Which risk category addresses situations where AI systems behave in ways inconsistent with their intended objectives?
Antwort: C
40. Frage
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