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. What is the Two-Model Approach to balancing accuracy and explainability? 1. Use one model during development and another during production 2. Deploy two models in different regions to meet local regulations 3. Use a high-performance model for predictions and a simpler model to generate explanations 4. Train two identical models and compare their outputs for consistency Correct! WHY: The Two-Model Approach uses a high-performance model for predictions and a simpler interpretable model to generate explanations. CONTEXT: This provides both accuracy and transparency without sacrificing either. REMEMBER: Two-Model Approach equals one model for accuracy plus one model for explanations. 2 / 7 2. A healthcare organization wants to deploy AI for patient triage. Which audience needs the simplest and most actionable explanations? 1. End users who need to understand and act on decisions 2. Developers who need to debug the model 3. Regulators who need to audit the system 4. Executives who need to approve the budget 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 automatically generate GDPR-compliant documentation 2. They are approved by all regulatory bodies for high-risk applications 3. They require no training data to make predictions 4. They achieve competitive accuracy while remaining fully interpretable by design 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 cannot be applied to production systems 3. They require access to training data to function 4. They show correlations with model behavior not true internal reasoning 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. A credit card company receives complaints about different credit limits for spouses with similar profiles. Without explainability capability what is the primary challenge they face? 1. They cannot change the credit limit thresholds 2. They cannot investigate or defend the algorithm even if it is actually fair 3. They cannot comply with PCI-DSS requirements 4. They cannot retrain the model on new data Correct! WHY: The Apple Card case demonstrated that without explanation capability you cannot investigate or defend your AI decisions even if they are actually fair. CONTEXT: This shows explainability is not just about compliance but about being able to investigate defend and improve AI systems. REMEMBER: No explainability equals no ability to defend your AI even when it is fair. 6 / 7 6. Why is the accuracy-explainability trade-off a genuine business challenge for managers? 1. There is no trade-off because modern XAI techniques have solved this problem 2. Explainable models are always more expensive to develop and maintain 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. What is the primary difference between AI transparency and AI explainability? 1. Transparency shows what goes into the system while explainability shows why a specific output was produced 2. Transparency is required by law while explainability is optional 3. There is no meaningful difference – both terms mean the same thing 4. Transparency applies to inputs and explainability applies to model architecture Correct! WHY: Transparency reveals what the AI system uses and how it was built while explainability reveals why a specific decision was made. CONTEXT: These concepts are often conflated but serve different purposes – transparency is about system disclosure and explainability is about decision reasoning. REMEMBER: Transparency equals showing your work and explainability equals explaining your reasoning. Your score isThe average score is 0% Restart quiz Download PDF Please leave this field empty🔐 The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We don’t spam! Read our privacy policy for more info. Thank you! Please check your inbox to confirm your subscription.
