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Question#1

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. Engage a third party to perform an audit.

C. Perform an impact assessment.

D. Create a bias bounty program.

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.

Question#2

Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

A. Tested and Effective.

B. Secure and Resilient.

C. Explainable and Interpretable.

D. Accountable and Transparent.

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework. The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust.
Reference: AIGP Body of Knowledge, NIST AI RMF section.

Question#3

The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?

A. Establishing explain ability criteria to responsibly source and use data to train Al systems.

B. Defining requirements specific to each industry sector and high-risk Al domain.

C. Focusing on Al technical design and post-deployment monitoring.

D. Balancing Al innovation with ethical considerations.

Explanation:
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).

Question#4

According to November 2023 White House Executive Order, which of the following best describes the guidance given to governmental agencies on the use of generative Al as a workplace tool?

A. Limit access to specific uses of generative Al.

B. Impose a general ban on the use of generative Al.

C. Limit access of generative Al to engineers and developers.

D. Impose a ban on the use of generative Al in agencies that protect national security.

Explanation:
The November 2023 White House Executive Order provides guidance that governmental agencies should limit access to specific uses of generative AI. This means that generative AI tools should be used in a controlled manner, where their applications are restricted to well-defined, approved use cases that ensure the security, privacy, and ethical considerations are adequately addressed. This approach allows for the benefits of generative AI to be harnessed while mitigating potential risks and abuses.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and risk management, and the White House Executive Order of November 2023.

Question#5

What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

A. To empower and train appropriate cross-functional teams.

B. To establish diverse, equitable and inclusive processes.

C. To determine responsibility for allocating budgetary resources.

D. To enable and encourage participation by external stakeholders.

Explanation:
The NIST AI Risk Management Framework’s Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross-functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization’s AI principles and ethical standards.
Reference: NIST AI Risk Management Framework documentation, Govern function section.

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