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. Why is attention visualization for LLMs considered limited as an explanation technique? 1. Attention mechanisms are not present in modern transformer models 2. Attention shows correlation not causation – high attention does not mean that input caused the output 3. Attention patterns cannot be visualized in real-time 4. Attention visualization only works for small language models Correct! WHY: Attention patterns show correlation not causation – high attention on an input does not necessarily mean that input caused the output. CONTEXT: This limitation means attention visualization should be used carefully not as definitive proof of model reasoning. REMEMBER: Attention shows what the model looked at not why it made a decision. 2 / 7 2. What is the Two-Model Approach to balancing accuracy and explainability? 1. Train two identical models and compare their outputs for consistency 2. Use one model during development and another during production 3. Use a high-performance model for predictions and a simpler model to generate explanations 4. Deploy two models in different regions to meet local regulations 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. 3 / 7 3. According to the EU AI Act what level of transparency is required for high-risk AI systems? 1. No transparency requirements for AI systems 2. Extensive documentation and explanation capability 3. Basic disclosure of the model type only 4. Full source code publication 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. 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 show correlations with model behavior not true internal reasoning 3. They require access to training data to function 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. 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 comply with PCI-DSS requirements 2. They cannot retrain the model on new data 3. They cannot investigate or defend the algorithm even if it is actually fair 4. They cannot change the credit limit thresholds 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. 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. NYC Local Law 144 4. SOC 2 Type II 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. 7 / 7 7. What is the primary difference between AI transparency and AI explainability? 1. There is no meaningful difference – both terms mean the same thing 2. Transparency is required by law while explainability is optional 3. Transparency shows what goes into the system while explainability shows why a specific output was produced 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 emptyš The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We donāt spam! Read our privacy policy for more info. Thank you! Please check your inbox to confirm your subscription.
