- (Topic 2)
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.
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?

1. A. Apply privacy-enhancing technologies to the data.
2. B. Identify fits and gaps in the combined data.
3. C. Ensure the data is labeled and formatted.
4. D. Evaluate the country of origin of the data.

Correct Answer: B
In the design phase of integrating data from different sources, identifying fits and gaps is crucial. This process involves understanding how well the data from the clinical research partner aligns with the healthcare network's existing data. It ensures that the combined data set is coherent and can be effectively used for training the AI algorithm. This step helps in spotting any discrepancies, inconsistencies, or missing data that might affect the performance and accuracy of the AI model. It directly addresses the integrity and compatibility of the data, which is foundational before applying any privacy-enhancing technologies, labeling, or evaluating the origin of the data. Reference: AIGP Body of Knowledge on Data Integration and Quality.

- (Topic 1)
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

1. A. Provide a just-in-time notice about the automated decision-making logic.
2. B. Define suitable measures to safeguard personal data.
3. C. Provide a right to review automated decision.
4. D. Establish a human-in-the-loop procedure.

Correct Answer: D
The GDPR requires that individuals have the right to not be subject to decisions based solely on automated processing, including profiling, unless specific exceptions apply. One effective control is to establish a human-in-the-loop procedure (D), ensuring human oversight and the ability to contest decisions. This goes beyond just-in- time notices (A), data safeguarding (B), or review rights (C), providing a more robust mechanism to protect individuals' rights.

- (Topic 1)
An EU bank intends to launch a multi-modal Al platform for customer engagement and automated decision-making assist with the opening of bank accounts. The platform has been subject to thorough risk assessments and testing, where it proves to be effective in not discriminating against any individual on the basis of a protected class.
What additional obligations must the bank fulfill prior to deployment?

1. A. The bank must obtain explicit consent from users under the privacy Directive.
2. B. The bank must disclose how the Al system works under the Ell Digital Services Act.
3. C. The bank must subject the Al system an adequacy decision and publish its appropriate safeguards.
4. D. The bank must disclose the use of the Al system and implement suitable measures for users to contest automated decision-making.

Correct Answer: D
Under the EU regulations, particularly the GDPR, banks using AI for decision-making must inform users about the use of AI and provide mechanisms for users to contest decisions. This is part of ensuring transparency and accountability in automated processing. Explicit consent under the privacy directive (A) and disclosing under the Digital Services Act (B) are not specifically required in this context. An adequacy decision is related to data transfers outside the EU (C).

- (Topic 2)
What is the technique to remove the effects of improperly used data from an ML system?

1. A. Data cleansing.
2. B. Model inversion.
3. C. Data de-duplication.
4. D. Model disgorgement.

Correct Answer: D
Model disgorgement is the technique used to remove the effects of improperly used data from an ML system. This process involves retraining or adjusting the model to eliminate any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's outputs are not influenced by data that was not meant to be used or was used incorrectly. Reference: AIGP Body of Knowledge on Data Management and Model Integrity.

- (Topic 1)
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model (“LLM”). In particular, ABC intends to use its historical customer data—including applications, policies, and claims—and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
What is the best strategy to mitigate the bias uncovered in the loan applications?

1. A. Retrain the model with data that reflects demographic parity.
2. B. Procure a third-party statistical bias assessment tool.
3. C. Document all instances of bias in the data set.
4. D. Delete all gender-based data in the data set.

Correct Answer: A
Retraining the model with data that reflects demographic parity is the best strategy to mitigate the bias uncovered in the loan applications. This approach addresses the root cause of the bias by ensuring that the training data is representative and balanced, leading to more equitable decision-making by the AI model.
Reference: The AIGP Body of Knowledge stresses the importance of using high-quality,
unbiased training data to develop fair and reliable AI systems. Retraining the model with balanced data helps correct biases that arise from historical inequalities, ensuring that the AI system makes decisions based on equitable criteria.