Spaces:

Soha85
/

RAI

Sleeping

RAI / client /src /data /quizDataEnhanced.ts

Simulator and scenarios added and fixed

bf7a424 verified 6 months ago

14.1 kB

// Enhanced quiz questions measuring solid conceptual understanding and application sequence
export const enhancedQuizQuestions = [
  // Fairness & Bias Mitigation - Application Sequence
  {
    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 (Demographic Parity vs. Equal Opportunity) with stakeholder input. (3) MITIGATE - Apply pre/in/post-processing. (4) VALIDATE - Test with affected communities. Skipping assessment leads to ineffective or harmful interventions.',
    relatedConcept: 'fairness-sequence'
  },
  {
    id: 'fairness-seq-2',
    pillar: 'Fairness & Bias Mitigation',
    difficulty: 'advanced',
    question: 'After discovering that your fairness metric choice (Demographic Parity) conflicts with Equal Opportunity for your hiring model, what should you do?',
    options: [
      'Ignore the conflict; pick the metric that gives highest accuracy',
      'Document the conflict, engage affected communities and legal team to explicitly choose which criterion matters most for your context',
      'Use both metrics simultaneously to satisfy all definitions',
      'Switch to Individual Fairness to avoid the conflict'
    ],
    correctAnswerIndex: 1,
    explanation: 'The Fairness Impossibility Theorem proves you cannot satisfy all fairness definitions when groups have different base rates. RAI requires explicit stakeholder choice about which principle governs your use case, documented with reasoning. This is a governance decision, not a technical one.',
    relatedConcept: 'fairness-tradeoff'
  },

  // Transparency & Explainability - Application Sequence
  {
    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. Audience-specific explanations are required.',
    relatedConcept: 'transparency-gates'
  },
  {
    id: 'transparency-seq-2',
    pillar: 'Transparency & Explainability',
    difficulty: 'advanced',
    question: 'Your fraud detection model achieves 0.88 faithfulness and 72% user comprehension. However, detailed explanations could help fraudsters game the system. What is the RAI-compliant approach?',
    options: [
      'Provide full explanations to all users; transparency is absolute',
      'Provide global explanations to fraud analysts; generalized local explanations to users; withhold specific feature weights',
      'Provide no explanations; security through obscurity',
      'Provide explanations only after fraud is confirmed'
    ],
    correctAnswerIndex: 1,
    explanation: 'RAI requires differential transparency: Different audiences get different levels of detail based on their role and the security implications. Fraud analysts need detailed explanations for investigation. Users get enough to understand why they were flagged but not enough to circumvent controls. This balances transparency with security.',
    relatedConcept: 'transparency-differential'
  },

  // Privacy & Security - Application Sequence
  {
    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, data poisoning) 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 relevant to your data and use case. (2) DEFENSE-IN-DEPTH - Layer multiple techniques (differential privacy + federated learning + k-anonymity). (3) TEST - Red team for membership inference and model inversion. (4) MONITOR - Track privacy metrics in production. Skipping threat modeling leads to over-engineering or under-protecting.',
    relatedConcept: 'privacy-sequence'
  },
  {
    id: 'privacy-seq-2',
    pillar: 'Privacy & Security',
    difficulty: 'advanced',
    question: 'Your privacy-utility Pareto frontier shows that achieving ε=0.5 differential privacy reduces model accuracy from 94% to 78%. What is the correct decision?',
    options: [
      'Use ε=2.0 to maintain 92% accuracy; privacy is less important than utility',
      'Use synthetic data only; real data is too risky',
      'Engage stakeholders (patients, clinicians, regulators) to explicitly choose acceptable privacy-utility tradeoff and document the decision',
      'Use ε=0.5 regardless of accuracy impact; privacy always wins'
    ],
    correctAnswerIndex: 2,
    explanation: 'The privacy-utility Pareto frontier illustrates an irreducible tradeoff: maximizing accuracy and maximizing privacy pull in opposite directions. RAI requires explicit stakeholder choice about acceptable risk. A 16% accuracy reduction may be unacceptable for clinical decisions but acceptable for screening. This is a governance decision, not a technical one.',
    relatedConcept: 'privacy-tradeoff'
  },

  // Safety & Reliability - Application Sequence
  {
    id: 'safety-seq-1',
    pillar: 'Safety & Reliability',
    difficulty: 'advanced',
    question: 'Your autonomous vehicle model passes 99.5% of test cases but fails catastrophically 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) so no single failure causes harm',
      '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: (1) Perception (detect edge cases), (2) Prediction (model uncertainty), (3) Planning (conservative decisions), (4) Control (hardware failsafes). Even if ML perception fails 0.5% of the time, other layers catch it. This is called "graceful degradation."',
    relatedConcept: 'safety-swiss-cheese'
  },
  {
    id: 'safety-seq-2',
    pillar: 'Safety & Reliability',
    difficulty: 'advanced',
    question: 'Your model detects a novel failure mode in production: it generates toxic outputs when given input in a non-English language. What is the correct incident response sequence?',
    options: [
      'Ignore it; the model was trained on English data',
      'Immediately disable the model, investigate root cause, implement guardrails, test fix, monitor for similar failures',
      'Add a language detection filter to block non-English inputs',
      'Retrain the model on multilingual data before redeploying'
    ],
    correctAnswerIndex: 1,
    explanation: 'RAI Incident Forensics (8-step process): (1) DETECT, (2) ALERT, (3) INVESTIGATE (root cause), (4) CONTAIN (stop propagation), (5) COMMUNICATE (notify stakeholders), (6) REMEDIATE (fix root cause), (7) LEARN (update safety boundaries), (8) VALIDATE (confirm fix works). Skipping steps leads to repeated failures.',
    relatedConcept: 'safety-incident'
  },

  // Accountability - Application Sequence
  {
    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 of decision factors, 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 (who is Responsible, Accountable, Consulted, Informed), (2) Human-in-the-loop for consequential decisions, (3) Accessible redressal mechanism (appeal + explanation + remedy), (4) Affected community representation in governance. The candidate must have recourse.',
    relatedConcept: 'accountability-redressal'
  },
  {
    id: 'accountability-seq-2',
    pillar: 'Accountability',
    difficulty: 'advanced',
    question: 'After a biased hiring decision, your organization faces a lawsuit. Who bears primary accountability?',
    options: [
      'The ML Engineer who built the model',
      'The Data Scientist who trained it',
      'The Chief AI Ethics Officer (CAIEO) who approved deployment',
      'All of the above; accountability is shared across the governance chain'
    ],
    correctAnswerIndex: 2,
    explanation: 'RAI Accountability Principle: Singular responsibility. The CAIEO has veto power and bears primary accountability for deployment decisions. Engineers are accountable for technical quality; CAIEO is accountable for governance. Diffused responsibility (everyone is responsible, so no one is) creates the "Accountability Vacuum"—the most common RAI failure.',
    relatedConcept: 'accountability-singular'
  },

  // Environmental Sustainability - Application Sequence
  {
    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 (model compression, renewable energy); 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 across training, inference, and operations. (2) OPTIMIZE - Model compression, quantization, renewable energy. (3) REPORT - Transparency on emissions. (4) OFFSET - Only after optimization. Jevons Paradox: efficiency improvements can increase total consumption if demand grows faster. RAI requires explicit limits.',
    relatedConcept: 'sustainability-sequence'
  },

  // Governance & Sequence - Application Sequence
  {
    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 projects, no formal rules. Stage 2 - Basic guidelines, inconsistently enforced. Stage 3 - Organization-wide policies, mandatory in dev cycle. Stage 4 - Data-driven governance, KPI dashboards. Stage 5 - Self-healing governance, AI monitors AI. Skipping stages creates brittle governance that collapses under pressure.',
    relatedConcept: 'governance-maturity'
  },
  {
    id: 'governance-seq-2',
    pillar: 'RAI Governance',
    difficulty: 'advanced',
    question: 'You are designing RAI governance gates for your organization. What is the correct sequence?',
    options: [
      'Gate 1 (Deployment) → Gate 2 (Training) → Gate 3 (Data) → Gate 4 (Production)',
      'Gate 1 (Data: PII scan) → Gate 2 (Training: fairness metrics) → Gate 3 (Deployment: red teaming) → Gate 4 (Production: runtime guardrails)',
      'Single gate at deployment; earlier gates are unnecessary',
      'Gates only for high-risk models; low-risk models bypass all gates'
    ],
    correctAnswerIndex: 1,
    explanation: 'RAI Governance Gates (4-stage lifecycle): Gate 1 (Data) - Data Minimization check for unauthorized PII. Gate 2 (Training) - Fairness Metric check (disparate impact ratio 0.8-1.2). Gate 3 (Deployment) - Red Teaming for dangerous outputs. Gate 4 (Production) - Runtime Guardrails for toxicity, drift, safety. Each gate is a decision point before advancing.',
    relatedConcept: 'governance-gates'
  }
];

// Export combined quiz questions
export const allQuizQuestions = [...enhancedQuizQuestions];

	// Enhanced quiz questions measuring solid conceptual understanding and application sequence
	export const enhancedQuizQuestions = [
	// Fairness & Bias Mitigation - Application Sequence
	{
	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 (Demographic Parity vs. Equal Opportunity) with stakeholder input. (3) MITIGATE - Apply pre/in/post-processing. (4) VALIDATE - Test with affected communities. Skipping assessment leads to ineffective or harmful interventions.',
	relatedConcept: 'fairness-sequence'
	},
	{
	id: 'fairness-seq-2',
	pillar: 'Fairness & Bias Mitigation',
	difficulty: 'advanced',
	question: 'After discovering that your fairness metric choice (Demographic Parity) conflicts with Equal Opportunity for your hiring model, what should you do?',
	options: [
	'Ignore the conflict; pick the metric that gives highest accuracy',
	'Document the conflict, engage affected communities and legal team to explicitly choose which criterion matters most for your context',
	'Use both metrics simultaneously to satisfy all definitions',
	'Switch to Individual Fairness to avoid the conflict'
	],
	correctAnswerIndex: 1,
	explanation: 'The Fairness Impossibility Theorem proves you cannot satisfy all fairness definitions when groups have different base rates. RAI requires explicit stakeholder choice about which principle governs your use case, documented with reasoning. This is a governance decision, not a technical one.',
	relatedConcept: 'fairness-tradeoff'
	},

	// Transparency & Explainability - Application Sequence
	{
	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. Audience-specific explanations are required.',
	relatedConcept: 'transparency-gates'
	},
	{
	id: 'transparency-seq-2',
	pillar: 'Transparency & Explainability',
	difficulty: 'advanced',
	question: 'Your fraud detection model achieves 0.88 faithfulness and 72% user comprehension. However, detailed explanations could help fraudsters game the system. What is the RAI-compliant approach?',
	options: [
	'Provide full explanations to all users; transparency is absolute',
	'Provide global explanations to fraud analysts; generalized local explanations to users; withhold specific feature weights',
	'Provide no explanations; security through obscurity',
	'Provide explanations only after fraud is confirmed'
	],
	correctAnswerIndex: 1,
	explanation: 'RAI requires differential transparency: Different audiences get different levels of detail based on their role and the security implications. Fraud analysts need detailed explanations for investigation. Users get enough to understand why they were flagged but not enough to circumvent controls. This balances transparency with security.',
	relatedConcept: 'transparency-differential'
	},

	// Privacy & Security - Application Sequence
	{
	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, data poisoning) 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 relevant to your data and use case. (2) DEFENSE-IN-DEPTH - Layer multiple techniques (differential privacy + federated learning + k-anonymity). (3) TEST - Red team for membership inference and model inversion. (4) MONITOR - Track privacy metrics in production. Skipping threat modeling leads to over-engineering or under-protecting.',
	relatedConcept: 'privacy-sequence'
	},
	{
	id: 'privacy-seq-2',
	pillar: 'Privacy & Security',
	difficulty: 'advanced',
	question: 'Your privacy-utility Pareto frontier shows that achieving ε=0.5 differential privacy reduces model accuracy from 94% to 78%. What is the correct decision?',
	options: [
	'Use ε=2.0 to maintain 92% accuracy; privacy is less important than utility',
	'Use synthetic data only; real data is too risky',
	'Engage stakeholders (patients, clinicians, regulators) to explicitly choose acceptable privacy-utility tradeoff and document the decision',
	'Use ε=0.5 regardless of accuracy impact; privacy always wins'
	],
	correctAnswerIndex: 2,
	explanation: 'The privacy-utility Pareto frontier illustrates an irreducible tradeoff: maximizing accuracy and maximizing privacy pull in opposite directions. RAI requires explicit stakeholder choice about acceptable risk. A 16% accuracy reduction may be unacceptable for clinical decisions but acceptable for screening. This is a governance decision, not a technical one.',
	relatedConcept: 'privacy-tradeoff'
	},

	// Safety & Reliability - Application Sequence
	{
	id: 'safety-seq-1',
	pillar: 'Safety & Reliability',
	difficulty: 'advanced',
	question: 'Your autonomous vehicle model passes 99.5% of test cases but fails catastrophically 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) so no single failure causes harm',
	'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: (1) Perception (detect edge cases), (2) Prediction (model uncertainty), (3) Planning (conservative decisions), (4) Control (hardware failsafes). Even if ML perception fails 0.5% of the time, other layers catch it. This is called "graceful degradation."',
	relatedConcept: 'safety-swiss-cheese'
	},
	{
	id: 'safety-seq-2',
	pillar: 'Safety & Reliability',
	difficulty: 'advanced',
	question: 'Your model detects a novel failure mode in production: it generates toxic outputs when given input in a non-English language. What is the correct incident response sequence?',
	options: [
	'Ignore it; the model was trained on English data',
	'Immediately disable the model, investigate root cause, implement guardrails, test fix, monitor for similar failures',
	'Add a language detection filter to block non-English inputs',
	'Retrain the model on multilingual data before redeploying'
	],
	correctAnswerIndex: 1,
	explanation: 'RAI Incident Forensics (8-step process): (1) DETECT, (2) ALERT, (3) INVESTIGATE (root cause), (4) CONTAIN (stop propagation), (5) COMMUNICATE (notify stakeholders), (6) REMEDIATE (fix root cause), (7) LEARN (update safety boundaries), (8) VALIDATE (confirm fix works). Skipping steps leads to repeated failures.',
	relatedConcept: 'safety-incident'
	},

	// Accountability - Application Sequence
	{
	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 of decision factors, 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 (who is Responsible, Accountable, Consulted, Informed), (2) Human-in-the-loop for consequential decisions, (3) Accessible redressal mechanism (appeal + explanation + remedy), (4) Affected community representation in governance. The candidate must have recourse.',
	relatedConcept: 'accountability-redressal'
	},
	{
	id: 'accountability-seq-2',
	pillar: 'Accountability',
	difficulty: 'advanced',
	question: 'After a biased hiring decision, your organization faces a lawsuit. Who bears primary accountability?',
	options: [
	'The ML Engineer who built the model',
	'The Data Scientist who trained it',
	'The Chief AI Ethics Officer (CAIEO) who approved deployment',
	'All of the above; accountability is shared across the governance chain'
	],
	correctAnswerIndex: 2,
	explanation: 'RAI Accountability Principle: Singular responsibility. The CAIEO has veto power and bears primary accountability for deployment decisions. Engineers are accountable for technical quality; CAIEO is accountable for governance. Diffused responsibility (everyone is responsible, so no one is) creates the "Accountability Vacuum"—the most common RAI failure.',
	relatedConcept: 'accountability-singular'
	},

	// Environmental Sustainability - Application Sequence
	{
	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 (model compression, renewable energy); 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 across training, inference, and operations. (2) OPTIMIZE - Model compression, quantization, renewable energy. (3) REPORT - Transparency on emissions. (4) OFFSET - Only after optimization. Jevons Paradox: efficiency improvements can increase total consumption if demand grows faster. RAI requires explicit limits.',
	relatedConcept: 'sustainability-sequence'
	},

	// Governance & Sequence - Application Sequence
	{
	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 projects, no formal rules. Stage 2 - Basic guidelines, inconsistently enforced. Stage 3 - Organization-wide policies, mandatory in dev cycle. Stage 4 - Data-driven governance, KPI dashboards. Stage 5 - Self-healing governance, AI monitors AI. Skipping stages creates brittle governance that collapses under pressure.',
	relatedConcept: 'governance-maturity'
	},
	{
	id: 'governance-seq-2',
	pillar: 'RAI Governance',
	difficulty: 'advanced',
	question: 'You are designing RAI governance gates for your organization. What is the correct sequence?',
	options: [
	'Gate 1 (Deployment) → Gate 2 (Training) → Gate 3 (Data) → Gate 4 (Production)',
	'Gate 1 (Data: PII scan) → Gate 2 (Training: fairness metrics) → Gate 3 (Deployment: red teaming) → Gate 4 (Production: runtime guardrails)',
	'Single gate at deployment; earlier gates are unnecessary',
	'Gates only for high-risk models; low-risk models bypass all gates'
	],
	correctAnswerIndex: 1,
	explanation: 'RAI Governance Gates (4-stage lifecycle): Gate 1 (Data) - Data Minimization check for unauthorized PII. Gate 2 (Training) - Fairness Metric check (disparate impact ratio 0.8-1.2). Gate 3 (Deployment) - Red Teaming for dangerous outputs. Gate 4 (Production) - Runtime Guardrails for toxicity, drift, safety. Each gate is a decision point before advancing.',
	relatedConcept: 'governance-gates'
	}
	];

	// Export combined quiz questions
	export const allQuizQuestions = [...enhancedQuizQuestions];