| export interface QuizQuestion { |
| id: string; |
| pillar: string; |
| difficulty: 'beginner' | 'intermediate' | 'advanced'; |
| question: string; |
| options: string[]; |
| correctAnswerIndex: number; |
| explanation: string; |
| relatedConcept?: string; |
| } |
|
|
| export const quizQuestions: QuizQuestion[] = [ |
| |
| { |
| id: 'fairness-1', |
| pillar: 'Fairness & Bias Mitigation', |
| difficulty: 'beginner', |
| question: 'Which fairness metric measures equal positive outcomes across all groups?', |
| options: [ |
| 'Equal Opportunity', |
| 'Demographic Parity', |
| 'Equalized Odds', |
| 'Individual Fairness' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Demographic Parity ensures that positive outcomes (e.g., loan approvals) are distributed equally across protected groups, regardless of other factors.', |
| relatedConcept: 'fairness' |
| }, |
| { |
| id: 'fairness-2', |
| pillar: 'Fairness & Bias Mitigation', |
| difficulty: 'beginner', |
| question: 'At what stage can bias be mitigated through re-weighting?', |
| options: [ |
| 'Pre-processing', |
| 'In-processing', |
| 'Post-processing', |
| 'All stages' |
| ], |
| correctAnswerIndex: 0, |
| explanation: 'Re-weighting assigns higher importance to underrepresented groups during data preparation (pre-processing) before the model sees the data.', |
| relatedConcept: 'fairness' |
| }, |
| { |
| id: 'fairness-3', |
| pillar: 'Fairness & Bias Mitigation', |
| difficulty: 'intermediate', |
| question: 'What is the Fairness Impossibility Theorem?', |
| options: [ |
| 'It is impossible to build fair AI systems', |
| 'You cannot satisfy all fairness definitions simultaneously when groups have different base rates', |
| 'Fairness metrics are always inaccurate', |
| 'Fairness requires zero bias' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'The Fairness Impossibility Theorem proves mathematically that you cannot satisfy all definitions of fairness at the same time if groups have different base rates. For example, achieving Demographic Parity may require violating Equal Opportunity.', |
| relatedConcept: 'fairness' |
| }, |
| { |
| id: 'fairness-4', |
| pillar: 'Fairness & Bias Mitigation', |
| difficulty: 'intermediate', |
| question: 'What is intersectional bias testing?', |
| options: [ |
| 'Testing bias in one protected attribute at a time', |
| 'Testing outcomes for subgroups like "Black women over 50" where compounding disadvantage hides in aggregated data', |
| 'Testing bias across different countries', |
| 'Testing bias in intersecting AI systems' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Intersectional bias testing evaluates outcomes for combinations of protected attributes (e.g., race + gender + age) because compounding disadvantage often hides when you only test single attributes.', |
| relatedConcept: 'fairness' |
| }, |
| { |
| id: 'fairness-5', |
| pillar: 'Fairness & Bias Mitigation', |
| difficulty: 'advanced', |
| question: 'When fairness constraints reduce overall accuracy by 8%, what is the correct RAI principle?', |
| options: [ |
| 'Abandon fairness constraints; accuracy is more important', |
| 'Fairness wins if protected group disparity exceeds 5% or legal requirement exists', |
| 'Use post-processing instead to preserve accuracy', |
| 'Accept the tradeoff and document it with stakeholder input' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI principle: Fairness wins if protected group disparity exceeds 5% or a legal requirement exists. An 8% accuracy reduction is acceptable if it prevents significant harm to protected groups.', |
| relatedConcept: 'fairness' |
| }, |
|
|
| |
| { |
| id: 'transparency-1', |
| pillar: 'Transparency & Explainability', |
| difficulty: 'beginner', |
| question: 'What is the difference between Global and Local Explainability?', |
| options: [ |
| 'Global is for models; Local is for data', |
| 'Global explains overall model logic; Local explains individual predictions', |
| 'Global is for regulators; Local is for users', |
| 'There is no difference' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Global Explainability answers "How does the model work?" while Local Explainability answers "Why did the model make this specific decision?" These require different techniques and serve different stakeholders.', |
| relatedConcept: 'transparency' |
| }, |
| { |
| id: 'transparency-2', |
| pillar: 'Transparency & Explainability', |
| difficulty: 'beginner', |
| question: 'What is the minimum faithfulness score for explanations before deployment?', |
| options: [ |
| '0.70', |
| '0.75', |
| '0.85', |
| '0.95' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'Explanations must have > 0.85 faithfulness (correlation between explanation and model internals) to ensure they accurately reflect the model\'s internal logic.', |
| relatedConcept: 'transparency' |
| }, |
| { |
| id: 'transparency-3', |
| pillar: 'Transparency & Explainability', |
| difficulty: 'intermediate', |
| question: 'Which explanation method is based on game theory?', |
| options: [ |
| 'LIME', |
| 'Integrated Gradients', |
| 'SHAP', |
| 'Saliency Maps' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'SHAP (SHapley Additive exPlanations) uses game theory to calculate each feature\'s contribution to predictions. It is considered the "gold standard" for explanation accuracy.', |
| relatedConcept: 'transparency' |
| }, |
| { |
| id: 'transparency-4', |
| pillar: 'Transparency & Explainability', |
| difficulty: 'intermediate', |
| question: 'What is the maximum acceptable robustness variance for explanations?', |
| options: [ |
| '5%', |
| '10%', |
| '15%', |
| '25%' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'Robustness: < 15% explanation variance under input perturbation (e.g., age +1 year, income +5%). A fragile explanation that jumps around is considered untrustworthy.', |
| relatedConcept: 'transparency' |
| }, |
| { |
| id: 'transparency-5', |
| pillar: 'Transparency & Explainability', |
| difficulty: 'advanced', |
| question: 'When should explanations be withheld or generalized?', |
| options: [ |
| 'Never; full transparency is always required', |
| 'Only when the model is inaccurate', |
| 'When full disclosure would compromise security, reveal training data, or enable adversarial manipulation', |
| 'When users request it' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'RAI requires withholding or generalizing explanations when: (1) Full disclosure compromises system security (fraud detection), (2) Explanation reveals sensitive training data (medical models), or (3) Detailed logic enables adversarial manipulation (content moderation).', |
| relatedConcept: 'transparency' |
| }, |
|
|
| |
| { |
| id: 'privacy-1', |
| pillar: 'Privacy & Security', |
| difficulty: 'beginner', |
| question: 'What is the primary goal of Data Minimization?', |
| options: [ |
| 'Reduce model size', |
| 'Collect only what is necessary', |
| 'Minimize storage costs', |
| 'Reduce training time' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Data Minimization is a privacy principle that requires collecting only the data necessary for the stated purpose, reducing exposure to privacy risks.', |
| relatedConcept: 'privacy' |
| }, |
| { |
| id: 'privacy-2', |
| pillar: 'Privacy & Security', |
| difficulty: 'beginner', |
| question: 'Which privacy technique trains models locally without centralizing data?', |
| options: [ |
| 'Differential Privacy', |
| 'Federated Learning', |
| 'k-Anonymity', |
| 'Encryption' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Federated Learning trains models on-device, keeping data local. Only model updates are shared, not raw data.', |
| relatedConcept: 'privacy' |
| }, |
| { |
| id: 'privacy-3', |
| pillar: 'Privacy & Security', |
| difficulty: 'intermediate', |
| question: 'What is a Membership Inference Attack?', |
| options: [ |
| 'An attack on user authentication', |
| 'Inferring whether specific data was in the training set', |
| 'Attacking the model\'s membership functions', |
| 'Inferring user identity from predictions' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'A Membership Inference Attack determines whether a specific record was used in model training. For example, an attacker could infer that a patient\'s medical data was in a healthcare model\'s training set.', |
| relatedConcept: 'privacy' |
| }, |
| { |
| id: 'privacy-4', |
| pillar: 'Privacy & Security', |
| difficulty: 'intermediate', |
| question: 'What is the Privacy-Utility Pareto Frontier?', |
| options: [ |
| 'The maximum accuracy achievable', |
| 'The minimum data needed for training', |
| 'The tradeoff between maximizing accuracy and maximizing privacy', |
| 'The boundary between private and public data' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'The Privacy-Utility Pareto Frontier illustrates an irreducible tradeoff: maximizing accuracy and maximizing privacy pull in opposite directions. Organizations must choose acceptable risk levels.', |
| relatedConcept: 'privacy' |
| }, |
| { |
| id: 'privacy-5', |
| pillar: 'Privacy & Security', |
| difficulty: 'advanced', |
| question: 'What is the recommended defense-in-depth approach for healthcare AI?', |
| options: [ |
| 'Use only differential privacy', |
| 'Use only federated learning', |
| 'Use only k-anonymity', |
| 'Combination of differential privacy + federated learning + encryption' |
| ], |
| correctAnswerIndex: 3, |
| explanation: 'Defense-in-depth requires multiple independent layers. For healthcare AI: differential privacy (adds noise), federated learning (train locally), and encryption (protect data in transit and at rest).', |
| relatedConcept: 'privacy' |
| }, |
|
|
| |
| { |
| id: 'safety-1', |
| pillar: 'Safety & Reliability', |
| difficulty: 'beginner', |
| question: 'What is the Swiss Cheese Model in AI Safety?', |
| options: [ |
| 'A model trained on Swiss data', |
| 'Multiple independent defense layers prevent cascading failures', |
| 'A model with holes in its logic', |
| 'A model that fails gracefully' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'The Swiss Cheese Model illustrates that each defense layer has failure modes. Safety only holds when layers are independent and overlapping with no single point of failure.', |
| relatedConcept: 'safety' |
| }, |
| { |
| id: 'safety-2', |
| pillar: 'Safety & Reliability', |
| difficulty: 'beginner', |
| question: 'What is Graceful Degradation?', |
| options: [ |
| 'Slowly reducing model accuracy over time', |
| 'Low confidence → escalate; Out-of-distribution → reject; Adversarial input → safe response', |
| 'Degrading model performance intentionally', |
| 'Removing features to simplify the model' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Graceful degradation ensures the system fails safely: when confidence is low, escalate to human; when input is out-of-distribution, reject; when adversarial, return safe response.', |
| relatedConcept: 'safety' |
| }, |
| { |
| id: 'safety-3', |
| pillar: 'Safety & Reliability', |
| difficulty: 'intermediate', |
| question: 'What is the risk classification for a system that could cause economic harm?', |
| options: [ |
| 'Green (Low)', |
| 'Yellow (Medium)', |
| 'Orange (High)', |
| 'Red (Critical)' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'Risk Classification: Red (death/critical harm), Orange (economic harm), Yellow (inconvenience), Green (minimal). Economic harm is classified as High (Orange).', |
| relatedConcept: 'safety' |
| }, |
| { |
| id: 'safety-4', |
| pillar: 'Safety & Reliability', |
| difficulty: 'intermediate', |
| question: 'What is the "Gray Zone" in AI Safety?', |
| options: [ |
| 'Uncertain model outputs', |
| 'Where humans must intervene when uncertainty is too high', |
| 'A color in the risk classification', |
| 'Ambiguous requirements' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'The Gray Zone is where model uncertainty exceeds acceptable thresholds. Humans must intervene to make the final decision rather than trusting the AI.', |
| relatedConcept: 'safety' |
| }, |
| { |
| id: 'safety-5', |
| pillar: 'Safety & Reliability', |
| difficulty: 'advanced', |
| question: 'What is the "Alignment Tax"?', |
| options: [ |
| 'A tax on AI systems', |
| 'The performance reduction from safety measures', |
| 'A cost imposed by regulators', |
| 'A fee for alignment certification' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'The Alignment Tax is the performance reduction (accuracy, speed, cost) incurred by implementing safety measures. It is an inescapable cost that must be explicitly managed.', |
| relatedConcept: 'safety' |
| }, |
|
|
| |
| { |
| id: 'accountability-1', |
| pillar: 'Accountability', |
| difficulty: 'beginner', |
| question: 'What is the primary purpose of a RACI matrix in AI governance?', |
| options: [ |
| 'Track project timelines', |
| 'Clarify responsibility and accountability for decisions', |
| 'Manage budget allocation', |
| 'Document code changes' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RACI clarifies who is Responsible, Accountable, Consulted, and Informed for each decision. This prevents diffused responsibility.', |
| relatedConcept: 'accountability' |
| }, |
| { |
| id: 'accountability-2', |
| pillar: 'Accountability', |
| difficulty: 'beginner', |
| question: 'What is the "Accountability Vacuum"?', |
| options: [ |
| 'When no one uses the AI system', |
| 'Diffused responsibility where everyone is responsible, so no one is', |
| 'When the model has no explainability', |
| 'When there\'s no budget for governance' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'The most common RAI failure is diffused responsibility. When multiple teams share blame, each points to the others, preventing clear accountability.', |
| relatedConcept: 'accountability' |
| }, |
| { |
| id: 'accountability-3', |
| pillar: 'Accountability', |
| difficulty: 'intermediate', |
| question: 'What is the role of a Chief AI Ethics Officer (CAIEO)?', |
| options: [ |
| 'Advise on ethical issues', |
| 'Veto power over deployment; escalation to CEO; reports to Board Risk Committee', |
| 'Ensure compliance with regulations', |
| 'Train employees on ethics' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'CAIEO has veto power over deployment, escalation authority to CEO, and reports to the Board Risk Committee (not CTO/CPO) to ensure independence.', |
| relatedConcept: 'accountability' |
| }, |
| { |
| id: 'accountability-4', |
| pillar: 'Accountability', |
| difficulty: 'intermediate', |
| question: 'What are the key elements of a Redressal Mechanism?', |
| options: [ |
| 'Accessibility, Timeliness, Human review, Explanation, Compensation, Escalation', |
| 'Just compensation for all affected users', |
| 'Regulatory approval and legal review', |
| 'Automated appeals process' |
| ], |
| correctAnswerIndex: 0, |
| explanation: 'Redressal Mechanism: Accessibility (free, in user\'s language) → Timeliness (48h acknowledgment, 30d resolution) → Human review (authority to override) → Explanation → Compensation → Escalation path.', |
| relatedConcept: 'accountability' |
| }, |
| { |
| id: 'accountability-5', |
| pillar: 'Accountability', |
| difficulty: 'advanced', |
| question: 'What is the minimum retention period for AI decision logs?', |
| options: [ |
| '1 year', |
| '3 years', |
| '6+ years or model lifetime + 2 years', |
| 'Indefinitely' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'Standard: 6+ years or model lifetime + 2 years (GDPR: duration of relationship). This ensures decisions can be audited and appealed long after deployment.', |
| relatedConcept: 'accountability' |
| }, |
|
|
| |
| { |
| id: 'sustainability-1', |
| pillar: 'Environmental Sustainability', |
| difficulty: 'beginner', |
| question: 'What is the largest component of AI carbon footprint?', |
| options: [ |
| 'Embodied carbon (hardware manufacturing)', |
| 'Operational carbon (electricity for training/inference)', |
| 'Water consumption', |
| 'E-waste' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Operational carbon (electricity) is typically 60-80% of total footprint, followed by embodied carbon (10-20%).', |
| relatedConcept: 'sustainability' |
| }, |
| { |
| id: 'sustainability-2', |
| pillar: 'Environmental Sustainability', |
| difficulty: 'beginner', |
| question: 'What is the Jevons Paradox in AI?', |
| options: [ |
| 'Smaller models are always more efficient', |
| 'Efficiency gains are erased by increased deployment scale', |
| 'Carbon offsets solve the sustainability problem', |
| 'AI training requires renewable energy' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Jevons Paradox: Cheaper inference invites wider deployment. A model using 50% less carbon per query but deployed to 10x more users has 5x total carbon increase.', |
| relatedConcept: 'sustainability' |
| }, |
| { |
| id: 'sustainability-3', |
| pillar: 'Environmental Sustainability', |
| difficulty: 'intermediate', |
| question: 'What is the carbon budget for product development AI?', |
| options: [ |
| '< 10 tCO₂', |
| '< 100 tCO₂', |
| '< 1,000 tCO₂', |
| 'Unlimited' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Carbon Budget Tiers: Experimental < 10 tCO₂ (team lead approval), Product < 100 tCO₂ (VP + Sustainability Officer), Production < 1,000 tCO₂ (C-suite + board).', |
| relatedConcept: 'sustainability' |
| }, |
| { |
| id: 'sustainability-4', |
| pillar: 'Environmental Sustainability', |
| difficulty: 'intermediate', |
| question: 'Which technique provides the greatest carbon reduction?', |
| options: [ |
| 'Pruning (10-50%)', |
| 'Knowledge Distillation (50-90%)', |
| 'Mixed Precision (30-50%)', |
| 'Sparse Attention (50-70%)' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Knowledge Distillation (training a smaller "student" model to mimic a large "teacher") achieves 50-90% carbon reduction, the highest among common techniques.', |
| relatedConcept: 'sustainability' |
| }, |
| { |
| id: 'sustainability-5', |
| pillar: 'Environmental Sustainability', |
| difficulty: 'advanced', |
| question: 'What is the critical principle for sustainable AI?', |
| options: [ |
| 'Maximize efficiency at any cost', |
| 'Use renewable energy and assume problem is solved', |
| 'Efficiency without efficacy is meaningless; pair efficiency with total consumption caps', |
| 'Carbon offsets solve all sustainability issues' |
| ], |
| correctAnswerIndex: 2, |
| explanation: 'Critical: A model using 10x less carbon but achieving 0% of intended benefit is waste, not sustainability. Efficiency metrics must be paired with total consumption caps.', |
| relatedConcept: 'sustainability' |
| }, |
|
|
| |
| { |
| id: 'governance-1', |
| pillar: 'RAI Governance', |
| difficulty: 'beginner', |
| question: 'What are the five ethical foundations of RAI?', |
| options: [ |
| 'Safety, Fairness, Transparency, Privacy, Accountability', |
| 'Autonomy, Non-Maleficence, Justice, Explicability, Dignity', |
| 'Efficiency, Accuracy, Scalability, Security, Compliance', |
| 'Innovation, Speed, Cost, Quality, Reliability' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Ethical Foundations: Autonomy (meaningful choice), Non-Maleficence (do no harm), Justice (fair distribution), Explicability (right to understand), Dignity (treat as ends, not data).', |
| relatedConcept: 'governance' |
| }, |
| { |
| id: 'governance-2', |
| pillar: 'RAI Governance', |
| difficulty: 'beginner', |
| question: 'What is the RAI Mindset?', |
| options: [ |
| 'A checklist at the end of a project', |
| 'Integrated into every design decision from day one', |
| 'A compliance function only', |
| 'Optional for low-risk systems' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Genuine RAI is not a checklist but integrated into every design decision from day one. The goal is not to "pass an audit" but to reduce real harm to real people.', |
| relatedConcept: 'governance' |
| }, |
| { |
| id: 'governance-3', |
| pillar: 'RAI Governance', |
| difficulty: 'intermediate', |
| question: 'What is the RAI Maturity Model progression?', |
| options: [ |
| 'Ad-hoc → Documented → Automated → Optimized', |
| 'Reactive → Aware → Developing → Managed → Proactive', |
| 'Beginner → Intermediate → Advanced → Expert', |
| 'Compliant → Certified → Audited → Approved' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Maturity Stages: Reactive (ad-hoc), Aware (basic guidelines), Developing (defined policies), Managed (data-driven), Proactive (self-healing governance).', |
| relatedConcept: 'governance' |
| }, |
| { |
| id: 'governance-4', |
| pillar: 'RAI Governance', |
| difficulty: 'intermediate', |
| question: 'What is the correct RAI implementation sequence?', |
| options: [ |
| 'Fairness → Privacy → Safety → Transparency → Accountability', |
| 'Assessment → Governance Foundation → Pillar Implementation → Operationalization → Continuous Improvement', |
| 'Deploy → Audit → Fix → Redeploy', |
| 'Compliance → Documentation → Monitoring → Reporting' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Implementation Sequence: (1) Assessment (inventory, maturity, risk), (2) Governance Foundation (CAIEO, audit), (3) Pillar Implementation (prioritized by risk), (4) Operationalization (CI/CD integration), (5) Continuous Improvement.', |
| relatedConcept: 'governance' |
| }, |
| { |
| id: 'governance-5', |
| pillar: 'RAI Governance', |
| difficulty: 'advanced', |
| question: 'What is the Wicked Problem in RAI?', |
| options: [ |
| 'A problem that is difficult to solve', |
| 'Solving one RAI challenge often worsens another; no single optimal solution exists', |
| 'A problem caused by malicious actors', |
| 'A problem without clear definition' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'The Wicked Problem: RAI challenges form a self-reinforcing system. Example: Solving agentic control increases alignment tax → increases regulatory complexity → drives shadow AI → increases skills gap.', |
| relatedConcept: 'governance' |
| }, |
|
|
| |
| { |
| id: 'fairness-seq-1', |
| pillar: 'Fairness & Bias Mitigation', |
| difficulty: 'advanced', |
| question: 'You are deploying a hiring AI that shows 12% lower accuracy for women in technical roles. What is the FIRST step in the RAI implementation sequence?', |
| options: [ |
| 'Deploy with post-processing adjustments to equalize thresholds', |
| 'Conduct an intersectional bias audit to understand if compounding disadvantage exists across race + gender + age', |
| 'Implement adversarial debiasing during model training', |
| 'Exclude gender from the model to prevent discrimination' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI sequence: (1) ASSESS - Audit for intersectional bias before any mitigation. (2) CHOOSE - Select fairness metric with stakeholder input. (3) MITIGATE - Apply pre/in/post-processing. (4) VALIDATE - Test with affected communities. Skipping assessment leads to ineffective interventions.' |
| }, |
| { |
| id: 'transparency-seq-1', |
| pillar: 'Transparency & Explainability', |
| difficulty: 'advanced', |
| question: 'A loan denial model has 0.92 faithfulness but only 45% user comprehension when shown SHAP explanations. What is the correct next step?', |
| options: [ |
| 'Deploy immediately; faithfulness > 0.85 is sufficient', |
| 'Simplify explanations to plain language and retest comprehension until > 70% is achieved', |
| 'Use LIME instead of SHAP to improve comprehension', |
| 'Hide explanations from users; show only to regulators' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'Explanation Quality Gates require: (1) Faithfulness > 0.85, (2) Robustness < 15% variance, AND (3) User comprehension > 70%. All three must pass. A technically faithful explanation that users cannot understand fails the gate.' |
| }, |
| { |
| id: 'privacy-seq-1', |
| pillar: 'Privacy & Security', |
| difficulty: 'advanced', |
| question: 'You are deploying a healthcare diagnostic AI trained on 1M patient records. Privacy advocates raise concerns about data memorization and membership inference attacks. What is the FIRST step?', |
| options: [ |
| 'Use differential privacy with ε=1.0 for defense-in-depth', |
| 'Conduct a threat model: identify specific attacks (membership inference, model inversion) and assess likelihood/impact', |
| 'Switch to synthetic data only; discard real patient data', |
| 'Implement federated learning to train on-device' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Privacy Sequence: (1) THREAT MODEL - Identify specific attacks. (2) DEFENSE-IN-DEPTH - Layer multiple techniques. (3) TEST - Red team for membership inference. (4) MONITOR - Track privacy metrics. Skipping threat modeling leads to over-engineering or under-protecting.' |
| }, |
| { |
| id: 'safety-seq-1', |
| pillar: 'Safety & Reliability', |
| difficulty: 'advanced', |
| question: 'Your autonomous vehicle model passes 99.5% of test cases but fails on 0.5% (edge cases with occluded pedestrians). What is the correct RAI approach?', |
| options: [ |
| 'Deploy with 99.5% accuracy; it exceeds human driver performance', |
| 'Implement the Swiss Cheese Model: multiple independent safety layers (perception + prediction + planning + control)', |
| 'Use only deterministic rule-based systems; never deploy ML for safety-critical decisions', |
| 'Add more training data until 100% accuracy is achieved' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Safety Principle: No single layer is perfect. The Swiss Cheese Model requires multiple independent defenses so no single failure causes harm. This is called "graceful degradation."' |
| }, |
| { |
| id: 'accountability-seq-1', |
| pillar: 'Accountability', |
| difficulty: 'advanced', |
| question: 'Your hiring AI denies a qualified candidate. The candidate requests an explanation and appeals process. What is the RAI-compliant accountability structure?', |
| options: [ |
| 'HR Manager makes final decision; no appeal possible', |
| 'Candidate can appeal to HR, receive written explanation, and request human review by independent panel', |
| 'CAIEO (Chief AI Ethics Officer) makes all hiring decisions', |
| 'Model decision is final; no human review' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Accountability requires: (1) Clear RACI matrix, (2) Human-in-the-loop for consequential decisions, (3) Accessible redressal mechanism (appeal + explanation + remedy), (4) Affected community representation in governance.' |
| }, |
| { |
| id: 'sustainability-seq-1', |
| pillar: 'Environmental Sustainability', |
| difficulty: 'advanced', |
| question: 'Your large language model training emits 50 tons of CO2. What is the first RAI step?', |
| options: [ |
| 'Deploy immediately; carbon footprint is acceptable for AI benefits', |
| 'Measure and report carbon footprint; identify efficiency improvements; set reduction targets', |
| 'Use only small models; never train large models', |
| 'Offset carbon with tree planting; continue as-is' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Sustainability Sequence: (1) MEASURE - Track carbon footprint. (2) OPTIMIZE - Model compression, renewable energy. (3) REPORT - Transparency on emissions. (4) OFFSET - Only after optimization. Jevons Paradox: efficiency can increase total consumption if demand grows faster.' |
| }, |
| { |
| id: 'governance-seq-1', |
| pillar: 'RAI Governance', |
| difficulty: 'advanced', |
| question: 'Your organization is implementing RAI from scratch. What is the correct maturity progression?', |
| options: [ |
| 'Stage 5 (Optimized): Deploy self-healing AI agents immediately', |
| 'Stage 1 (Reactive) → Stage 2 (Aware) → Stage 3 (Defined) → Stage 4 (Managed) → Stage 5 (Optimized)', |
| 'Skip stages 1-2; go directly to Stage 3 (Defined) with organization-wide policies', |
| 'All stages simultaneously across different teams' |
| ], |
| correctAnswerIndex: 1, |
| explanation: 'RAI Maturity Model (5 stages): Stage 1 - Ad-hoc, no formal rules. Stage 2 - Basic guidelines, inconsistently enforced. Stage 3 - Organization-wide policies. Stage 4 - Data-driven governance. Stage 5 - Self-healing governance. Skipping stages creates brittle governance.' |
| } |
| ]; |
| export const quizzesByPillar = quizQuestions.reduce((acc, q) => { |
| if (!acc[q.pillar]) acc[q.pillar] = []; |
| acc[q.pillar].push(q); |
| return acc; |
| }, {} as Record<string, QuizQuestion[]>); |
|
|
| export const pillars = [ |
| 'Fairness & Bias Mitigation', |
| 'Transparency & Explainability', |
| 'Privacy & Security', |
| 'Safety & Reliability', |
| 'Accountability', |
| 'Environmental Sustainability', |
| 'RAI Governance' |
| ]; |
|
|
