AI0-001 AI Governance and Ethics Practice Test 5 — 15 Questions

Question 1

A hospital is implementing an AI system to analyze patient X-rays for potential fractures. The hospital must comply with HIPAA regulations. Which privacy-preserving technique allows the model to be trained on data from multiple hospitals without sharing raw patient data?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Federated learning. Federated learning trains model copies across multiple sites and aggregates only model updates (gradients) to a central server, keeping patient data local. Differential privacy adds noise to data but does not inherently prevent data sharing. Anonymisation and pseudonymisation still require sharing data, albeit de-identified.

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Differential privacy

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Data anonymisation

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Data pseudonymisation

Question 2

An AI ethics board is reviewing a model that recommends criminal sentencing lengths. They want to ensure that the model's false positive rates for different demographic groups are equal. Which fairness metric should they use?

Accepted Answer

Equalized odds. Equalized odds requires that the model's true positive rates and false positive rates are equal across groups. Demographic parity only requires equal selection rates. Individual fairness ensures similar individuals are treated similarly but does not define group rates. Calibration ensures predicted probabilities match actual outcomes for each group but does not enforce equal error rates.

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Calibration

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Individual fairness

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Demographic parity

Question 3

A company uses an AI system to screen job applicants. Under the GDPR, if the system makes automated decisions that have a legal or similarly significant effect on individuals, the data subject has the right to obtain an explanation of the decision. What is this right commonly called?

Accepted Answer

Right to explanation. The GDPR's right to explanation is not explicitly named in the regulation but is widely referred to as such in the context of automated decision-making under Article 22. The other options are other data subject rights under GDPR but do not specifically address explanation of decisions.

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Right to erasure (right to be forgotten)

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Right to object

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Right to data portability

Question 4

An AI risk manager is applying the NIST AI Risk Management Framework (AI RMF). In which function would the organization establish a risk management process and assign roles and responsibilities for AI oversight?

Accepted Answer

Govern. The Govern function in the NIST AI RMF is specifically designed to establish organizational structures, policies, and accountability mechanisms for AI risk management. This includes defining roles and responsibilities, setting risk management processes, and ensuring oversight across the AI lifecycle. The other functions (Map, Measure, Manage) focus on different aspects such as understanding context, assessing risks, and treating risks, respectively.

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Map

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Manage

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Measure

Question 5

A company is considering using an open-source large language model for a commercial application. Which intellectual property consideration is MOST important when deciding between open-source and proprietary models?

Accepted Answer

The model's license terms and any restrictions on commercial use. Understanding the model's license is critical because open-source licenses can have restrictions on commercial use, attribution requirements, or copyleft provisions that affect how the model can be used and distributed. The other options are less directly relevant to the open vs proprietary decision.

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The model's accuracy on benchmark tasks

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The size of the model's parameter count

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The model's training data provenance

Question 6

A data scientist is evaluating a binary classifier for a hiring tool. They compute demographic parity and find that the selection rate for Group A is 0.2 and for Group B is 0.4. Which action would MOST directly address this disparity?

Accepted Answer

Retrain the model with a fairness constraint that enforces demographic parity. Demographic parity requires equal selection rates. Retraining with a fairness constraint that enforces demographic parity directly adjusts the model to achieve equal rates. Rebalancing the dataset (if the disparity stems from imbalanced labels) might help, but it does not guarantee parity. Modifying thresholds can also achieve parity, but post-processing without retraining may degrade other metrics; retraining with constraint is more direct.

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Use a different evaluation metric such as equalized odds

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Remove the sensitive attribute from the training data

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Collect more data for Group A to increase its representation

Question 7

A company is developing an AI-driven recruitment tool. To comply with the EU AI Act's high-risk requirements, which TWO of the following are mandatory obligations?

Accepted Answer

Maintain technical documentation and risk management. High-risk AI systems under the EU AI Act must implement human oversight mechanisms and maintain a technical documentation and risk management system. Transparency disclosure is required, but not necessarily to users in all cases (it depends on context). The other options are not mandatory for all high-risk systems.

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Publish the model's source code publicly

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Conduct a data protection impact assessment (DPIA)

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Disclose to users that they are interacting with an AI system

Question 8

A company deploys an AI system for loan approvals. The EU AI Act classifies this as high-risk. Which human oversight requirement applies?

Accepted Answer

Human-in-the-loop (HITL) mechanism. The EU AI Act requires human-in-the-loop (HITL) oversight for high-risk AI systems like loan approval, meaning a human must be able to intervene and override the system's decisions during operation. This ensures that automated decisions can be reviewed and corrected in real-time, preventing fully autonomous outcomes in critical areas such as credit scoring.

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Human-in-command approach

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Human-on-the-loop oversight

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Automated decision-making without human review

Question 9

A data scientist needs to explain why a black-box model denied a loan application. Which explainability technique generates local feature importance values using a simpler interpretable model around the prediction?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct technique because it generates local feature importance values by fitting a simpler, interpretable model (e.g., linear regression or decision tree) around the prediction of the black-box model. This allows the data scientist to explain why a specific loan application was denied by identifying which features (e.g., income, credit score) most influenced that particular decision. Unlike global methods, LIME focuses on the local neighborhood of the instance, making it ideal for explaining individual predictions.

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Model card

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Attention visualisation

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SHAP values

Question 10

A bank uses an AI system for credit scoring. To meet fairness requirements, they want to ensure the model predicts similar outcomes for individuals who are similar with respect to the target variable, regardless of protected attributes. Which fairness metric addresses this?

Accepted Answer

Individual fairness. Individual fairness requires that similar individuals (with respect to legitimate features) receive similar predictions. Demographic parity requires equal selection rates across groups. Equalized odds requires equality of true positive and false positive rates. Calibration ensures predicted probabilities match actual outcomes across groups.

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Demographic parity

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Calibration

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Equalised odds

Question 11

A company trains a large language model on a dataset that includes copyrighted books. Under current legal interpretations, which statement about copyright infringement is MOST accurate?

Accepted Answer

Training on copyrighted data without permission likely infringes copyright, though fair use may be a defense.. Training on copyrighted works without permission is generally considered copyright infringement, unless a specific exception applies (e.g., fair use in the US). Fair use is determined on a case-by-case basis and is not automatically granted. Using only public domain works avoids infringement. The EU AI Act does not provide blanket permission.

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Training on copyrighted data is generally permissible under the EU AI Act.

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Training on copyrighted data is always covered by fair use in the US.

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Training on copyrighted data is allowed as long as the model is not used commercially.

Question 12

An AI system used for hiring is found to have a disparate impact on a protected group. What is the first step in addressing this under the NIST AI RMF?

Accepted Answer

Map - understand the use case and potential impacts. The NIST AI RMF follows a sequence of functions: Govern, Map, Measure, and Manage. When a disparate impact is discovered, the first step is to Map the context, which involves understanding the specific use case, the AI system's operational design, and the potential sources of bias. Without this foundational understanding, any subsequent governance, measurement, or mitigation efforts would be misdirected or ineffective.

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Govern - establish governance policies

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Manage - implement mitigation strategies

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Measure - evaluate the fairness metrics

Question 13

An AI model for skin cancer detection achieves high accuracy but performs poorly on dark skin tones. The team wants to evaluate whether the model is calibrated across skin tones. Which fairness metric should they use?

Accepted Answer

Calibration. Calibration is the correct metric because it directly measures whether the predicted probabilities of skin cancer match the actual outcomes across different skin tones. A model can have high overall accuracy but be miscalibrated for a subgroup if its confidence scores are systematically over- or under-confident for that group. In this scenario, the team needs to check if the model's risk scores are equally reliable for dark skin tones as for light skin tones, which is exactly what calibration assesses.

Answer

Equalised odds

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Demographic parity

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Individual fairness

Question 14

A company deploys an AI chatbot that generates product descriptions. The company wants to be transparent about AI-generated content. Which practice should they follow?

Accepted Answer

Clearly label AI-generated content as such. Transparency about AI-generated content is a core principle of AI governance and ethics. Labeling AI-generated outputs as such allows users to make informed decisions about the content they consume, aligning with responsible AI practices.

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Publish a model card, but not label individual outputs

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Add an invisible watermark but do not inform users

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Do not disclose that content is AI-generated to avoid user confusion

Question 15

During an audit of an AI system, the auditor requests documentation on the model's intended use, performance metrics, and limitations. Which tool is designed to provide this information in a standardized format?

Accepted Answer

Model card. Model cards are standardized documentation sheets that describe a model's intended use, performance, limitations, and other details. SHAP and LIME are explainability tools. Data cards describe datasets.

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SHAP values

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LIME

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Data card