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NEW QUESTION # 54
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 to best mitigate its risk of reputational harm from using the AI tool?
Answer: A
Explanation:
Note: This is the same scenario and question as Question 21 and thus has the same 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." The AI Governance in Practice Report 2024 further reinforces:
"Ongoing monitoring and testing post-deployment allows organizations to catch and correct unintended impacts... especially important in HR and hiring contexts."
NEW QUESTION # 55
Retraining an LLM can be necessary for all of the following reasons EXCEPT?
Answer: A
Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.
NEW QUESTION # 56
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?
Answer: D
Explanation:
An AI system'sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.
From theAI Governance in Practice Report 2025:
"There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization's role, footprint, broader risk-governance profile and maturity." (p. 4)
"AI governance starts with defining the corporate strategy for AI... and formulating policy standards and operational procedures to reflect industry, use case, and location." (p. 11) A- Organizational accountability is broader and not directly scoped by industry or function.
C- Diversity of data sources is tied to data strategy.
D- Explainability is more influenced by model type, not use context.
NEW QUESTION # 57
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The marketing company and its tech provider have taken reasonable steps to govern the AI's use, including legal disclosures, impact assessments, and bias mitigation. However, the company wants to takeone more stepto improve governance and reduce risks related to ongoing oversight and accountability.
While the marketing agency took steps to mitigate its risks, the best additional step would be to:
Answer: C
Explanation:
The correct answer isD. Forming adedicated governance committeeensures continuous oversight, role clarity, and accountability throughout the AI lifecycle.
From the AIGP ILT Guide - Governance Structures:
"Organizations using AI in high-impact scenarios should establish a governance body responsible for oversight of risk, compliance, and ethical alignment." Also reflected in AI Governance in Practice Report2025:
"Committees support cross-functional decision-making, provide guidance for updates, and maintain accountability. This is especially critical for high-stakes applications like marketing to diverse audiences." Options A, B, and C are valid supplementary actions, butDoffers a long-term and systematic governance mechanism.
NEW QUESTION # 58
What is the primary purpose of an AI impact assessment?
Answer: A
Explanation:
The correct answer is D. AI Impact Assessments are primarily used to identify and manage risks and harms associated with AI systems.
From the AIGP Body of Knowledge:
"The goal of an AI impact assessment is to ensure that risks are identified, evaluated, and mitigated prior to or during development and deployment." As further confirmed in the AI Governance in Practice Report 2024 (Part III):
"Risk-based tools like DPIAs and Algorithmic Impact Assessments help identify potential risks to individuals and society, enabling organizations to implement mitigation plans and safeguards." While benefits may be noted in such assessments, the core objective is to manage risks and promote responsible AI.
NEW QUESTION # 59
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