AI Governance and Ethics

A healthcare AI system uses patient data to predict disease risk. To comply with HIPAA and reduce the risk of re-identification, which technique should be applied to the training data before model development?

An AI team is developing a model that approves loan applications. The dataset contains historical loan decisions where a protected group was disproportionately denied loans. The team wants to ensure the model does not perpetuate this bias. Which fairness metric should be used during validation to directly measure whether the model's positive prediction rate is equal across groups?

A company is deploying an AI system that screens job applications. According to the EU AI Act, this system is likely classified as high-risk because it affects employment opportunities. Which requirement must the company implement for high-risk AI systems?

A data scientist is using SHAP to explain a complex ensemble model's predictions. A business stakeholder asks why a particular prediction was made. The data scientist wants to show the most influential features for that single prediction. Which SHAP visualisation is most appropriate?

A financial institution needs to deploy a credit scoring model that is interpretable to regulators. The model must provide clear reasons for each decision. Which model type should the institution choose?

A company is training a large language model from scratch and wants to minimise its environmental impact. Which practice aligns with green AI principles?

An organisation is developing an AI policy. According to the NIST AI RMF, which function involves establishing policies and procedures to ensure the organisation governs AI responsibly?

A company uses AI to generate marketing images. They want to ensure that the images are clearly identified as AI-generated to comply with transparency obligations. Which approach is most effective?

A hospital wants to train a diagnostic model using data from multiple hospitals without sharing raw patient data. Which technique allows model training across decentralised data while preserving privacy?

Under the GDPR, individuals have the right to not be subject to a decision based solely on automated processing if it produces legal effects. Which of the following is a typical safeguard that organisations must provide to comply with this right?

A company is evaluating a vendor's AI system for hiring. The vendor claims the system is fair because it achieves demographic parity. However, the company discovers that the system has significantly different false positive rates across groups. Which fairness issue does this indicate?

An AI system trained on historical medical records shows that certain racial groups have higher predicted risk for a disease. The data reflects real-world differences in diagnosis rates due to unequal access to healthcare. Which type of bias is this?

A company is implementing an AI ethics board. Which TWO responsibilities should the board typically have?

A company is deploying an AI system that falls under the EU AI Act's high-risk category. Which THREE requirements must the company fulfill?

A data scientist is using LIME to explain a black-box model. Which TWO characteristics of LIME are true?

A healthcare AI system diagnosing diabetic retinopathy from retinal images shows high accuracy overall but significantly lower recall for patients with darker skin tones. Which fairness metric would BEST capture this disparity by comparing true positive rates across groups?

A data scientist needs to explain a single prediction from a complex ensemble model to a business stakeholder. Which technique generates local, interpretable explanations by perturbing input features and fitting a simple surrogate model?

A company deploys an AI resume screening tool. It learns from historical hiring data where most successful hires were male, leading the model to favour male candidates. Which type of bias is this primarily?

Under the EU AI Act, an AI system that uses subliminal techniques to materially distort a person's behaviour, causing psychological or physical harm, would be classified under which risk tier?

A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?

A model's predicted probabilities are well-calibrated overall but the model systematically assigns higher probabilities to one demographic group even when the actual outcome likelihood is the same. Which fairness issue is present?

A company using an AI-based hiring tool receives a candidate request for explanation of an automated rejection. Which GDPR principle is most directly relevant?

Which NIST AI RMF function involves identifying the context, risks, and potential impacts of an AI system, including mapping the AI lifecycle and stakeholders?

A company is required to disclose that content has been generated or significantly modified by AI. Which practice directly addresses this transparency obligation?

A data scientist is training a large language model and wants to reduce the carbon footprint. Which practice is MOST effective for reducing energy consumption during training?

An organisation is deploying an AI system for credit scoring, which is considered high-risk under the EU AI Act. Which requirement is NOT typically mandated for high-risk systems?

Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?

A company is forming an AI ethics board to oversee the development of a high-stakes AI system for bail decision recommendations. Which THREE responsibilities should the board primarily undertake?

A financial institution wants to use AI for loan approvals and must comply with fair lending laws. Which TWO practices should the institution adopt to mitigate bias and ensure compliance?

An organisation is developing an AI system that will be used to evaluate teacher performance in public schools. To ensure responsible use, which TWO governance elements should be in place?

A healthcare AI startup is developing a model to predict patient readmission risk. The model will be used to allocate post-discharge resources. Which regulatory framework primarily governs the use of patient data in this scenario?

A data scientist is training a resume screening model to rank job applicants. The training data includes historical hiring decisions from the past 10 years. The company wants to avoid unfair bias against underrepresented groups. Which type of bias is most likely present in the training data?

A financial institution is deploying an AI system to approve personal loans. To comply with the EU AI Act's high-risk AI requirements, the bank must ensure meaningful human oversight. Which implementation BEST satisfies this requirement?

A data scientist needs to explain why a specific loan application was rejected by a tree-based model. The model is complex and not inherently interpretable. Which method should the data scientist use to provide a local explanation for this single prediction?

A company wants to train a language model on sensitive customer data without transferring the raw data to a central server. Which privacy-preserving technique should they use?

An AI governance team is implementing the NIST AI Risk Management Framework. They have identified a high-risk AI system and are in the 'Measure' function. Which activity is most appropriate for this function?

A company uses an AI system to generate marketing images. They are concerned about copyright ownership of the generated content. According to current US copyright law, who typically owns the copyright for AI-generated work?

A company is evaluating fairness metrics for a hiring model. They want to ensure that the model has similar true positive rates (TPR) across demographic groups. Which fairness metric should they use?

A company wants to reduce the carbon footprint of training large AI models. Which practice is MOST effective for achieving 'Green AI'?

A healthcare AI model is subject to GDPR because it processes data of EU patients. The model makes automated decisions about treatment plans. Under GDPR, patients have the right to obtain an explanation of the decision. Which approach BEST satisfies this 'right to explanation'?

A company is developing an AI policy. Which of the following should be included to ensure accountability for AI-driven decisions?

An AI system is being deployed to detect deepfakes in video content. To comply with transparency obligations, what should the company implement?

A company is adopting the EU AI Act's risk-based approach. They are classifying an AI system used for credit scoring. Which TWO risk tiers apply to credit scoring according to the Act?

A company is conducting a vendor AI assessment for a third-party natural language processing service. They need to ensure the vendor's AI governance practices align with their own. Which THREE areas should they evaluate?

A data scientist is using differential privacy to protect individual privacy in a training dataset. Which TWO actions are correct implementations of differential privacy?

A data scientist discovers that a model trained to predict loan defaults is denying loans at a higher rate for a particular demographic group. Which type of bias is MOST likely present?

A healthcare AI startup is developing a model to predict patient readmission risk. The company wants to ensure the model's decisions can be understood by clinicians. Which explainability technique provides local, model-agnostic explanations by fitting a simple surrogate model around a prediction?

A financial institution wants to deploy an AI system that automates loan approvals. Under the EU AI Act, this system would be classified as high-risk. Which of the following is a MANDATORY requirement for high-risk AI systems before market placement?

A company is training a large language model and wants to reduce its carbon footprint. Which practice is MOST effective for reducing training energy consumption while maintaining model quality?

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?

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?

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?

A researcher is developing a generative AI model that creates realistic images. To comply with emerging transparency obligations, the researcher must ensure that AI-generated content can be identified as such. Which technique embeds a digital identifier directly into the content that survives compression and cropping?

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?

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?

A city government uses an AI system to allocate limited social services resources. To ensure fairness, they want to implement human oversight for high-stakes decisions. Which mechanism allows a human to review and potentially override the AI's decision before it is executed?

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?

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?

A research lab is training a large language model and wants to minimize its environmental impact. Which THREE practices are most effective for reducing the carbon footprint of model training?

A bank wants to ensure its credit scoring model is fair across demographic groups. The model currently uses features like zip code, income, and credit history. To mitigate potential bias, which TWO actions should the data science team prioritize?

A healthcare AI system uses patient data to predict disease risk. To comply with HIPAA, which privacy technique should be applied to the training data?

An AI team notices that their hiring model consistently selects male candidates over equally qualified female candidates. Analysis shows the training data contains past hiring decisions where men were predominantly hired. Which type of bias is the root cause?

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

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?

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?

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?

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?

A hospital wants to train a diagnostic AI model using data from multiple hospitals without sharing raw patient data. Which privacy-preserving technique allows collaborative training while keeping data local?

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?

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?

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?

A social media platform uses an AI system to moderate content. The system incorrectly flags legitimate posts as hate speech, disproportionately affecting minority groups. Which type of bias is likely present?

A company is developing an AI policy for internal use. According to corporate AI governance best practices, which TWO components are essential for the policy?

A healthcare AI system is subject to GDPR because it processes patient data. Which THREE requirements must the system satisfy?

A company wants to adopt green AI practices to reduce the environmental impact of training large models. Which TWO actions are most effective?

A data scientist notices that a hiring model systematically scores female candidates lower than male candidates with similar qualifications. The training data was collected from past hiring decisions where the company historically hired more men. Which type of AI bias is most directly demonstrated?

A healthcare AI startup is developing a diagnostic tool that uses patient data to predict disease risk. To comply with HIPAA and minimize privacy risks while still training accurate models, which privacy-preserving technique should they prioritize?

A financial institution deploys an AI model for loan approval. To meet regulatory requirements under the EU AI Act for high-risk AI systems, they must ensure human oversight. Which implementation best satisfies the requirement for meaningful human intervention?

An insurance company uses a black-box deep learning model to set premiums. Regulators demand explanation for individual decisions. Which interpretability technique should the data science team apply to generate local explanations for each prediction?

A data governance team is developing an AI policy for a large corporation. Which TWO elements are essential for a responsible AI governance framework?

A company is deploying a generative AI system that produces text content. To comply with emerging transparency obligations, which THREE measures should they implement?

A hospital uses an AI system to prioritize patient treatment. They want to ensure fairness across demographic groups. Which TWO fairness metrics should they apply to evaluate the model?

A startup is training a large language model and wants to reduce its environmental impact. Which TWO practices are considered green AI?

A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?

Under the EU AI Act, an AI system used for credit scoring is classified as high-risk. Which THREE obligations apply to the deployer of such a system?