AI Transparency & Explainability: Manager’s Guide | QuizBy Eyal Doron / December 6, 2025 / 1 minute of reading AI Transparency & Explainability: Manager’s Guide | Quiz 1 / 7 1. According to the EU AI Act what level of transparency is required for high-risk AI systems? 1. Full source code publication 2. Basic disclosure of the model type only 3. Extensive documentation and explanation capability 4. No transparency requirements for AI systems Correct! WHY: The EU AI Act requires extensive documentation and explanation capability for high-risk AI systems. CONTEXT: Requirements scale with risk level and providers must ensure systems can be understood by operators. REMEMBER: EU AI Act equals risk-scaled transparency – higher risk equals more documentation and explanation capability. 2 / 7 2. A healthcare organization wants to deploy AI for patient triage. Which audience needs the simplest and most actionable explanations? 1. Executives who need to approve the budget 2. End users who need to understand and act on decisions 3. Developers who need to debug the model 4. Regulators who need to audit the system Correct! WHY: End users need simple actionable explanations focusing on key factors in plain language. CONTEXT: Different audiences need different explanation depths – users need simplicity while regulators need documentation and developers need technical details. REMEMBER: Match explanation complexity to your audience – simple for users technical for developers. 3 / 7 3. What makes glass-box models like EBMs and GAMs valuable for high-stakes decisions? 1. They require no training data to make predictions 2. They achieve competitive accuracy while remaining fully interpretable by design 3. They automatically generate GDPR-compliant documentation 4. They are approved by all regulatory bodies for high-risk applications Correct! WHY: Glass-box models achieve competitive accuracy while remaining fully interpretable by design. CONTEXT: Modern interpretable ML has narrowed the accuracy gap making these viable alternatives to black box models for critical applications. REMEMBER: Glass-box models offer both competitive accuracy and built-in explainability. 4 / 7 4. What is the key limitation of post-hoc explanation methods like LIME and SHAP? 1. They only work with neural network models 2. They require access to training data to function 3. They show correlations with model behavior not true internal reasoning 4. They cannot be applied to production systems Correct! WHY: LIME and SHAP are approximations of model behavior not true windows into internal reasoning. CONTEXT: They show correlation with model outputs not necessarily causation or true internal reasoning. REMEMBER: Post-hoc methods are useful tools not ground truth about why the model decided. 5 / 7 5. What does SHAP use to determine feature importance in AI predictions? 1. Game theory concepts specifically Shapley values 2. Neural network attention patterns 3. Random sampling of input features 4. Decision tree node splits Correct! WHY: SHAP uses game theory concepts specifically Shapley values to assign importance to each feature for a prediction. CONTEXT: SHAP provides theoretically grounded and consistent feature importance making it a popular choice for explaining black box models. REMEMBER: SHAP equals Shapley values equals game theory for feature importance. 6 / 7 6. Why is the accuracy-explainability trade-off a genuine business challenge for managers? 1. Explainable models are always more expensive to develop and maintain 2. There is no trade-off because modern XAI techniques have solved this problem 3. Regulators require all models to have the same level of explainability regardless of accuracy 4. The most accurate models are often the least explainable creating tension between performance and interpretability Correct! WHY: The most accurate models like deep neural networks are often the least explainable while interpretable models may sacrifice accuracy. CONTEXT: This creates a real business decision about when accuracy is worth the explainability cost. REMEMBER: Match explainability requirements to decision stakes – high consequences need more explainability. 7 / 7 7. Which regulation grants individuals the right to meaningful information about the logic involved in automated decisions? 1. EU AI Act Article 1 2. GDPR Article 22 3. SOC 2 Type II 4. NYC Local Law 144 Correct! WHY: GDPR Article 22 specifically addresses automated decision-making and requires organizations to provide meaningful information about the logic involved. CONTEXT: This right applies when AI makes decisions about credit employment insurance or similar consequential outcomes. REMEMBER: GDPR Article 22 is your right to explanation for automated decisions. 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