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. Your organization is implementing AI explainability. What is the FIRST step in the implementation framework? 1. Documenting all existing model architectures 2. Purchasing LIME and SHAP software licenses 3. Risk assessment to classify AI applications by explanation requirements 4. Training all staff on explainability concepts Correct! WHY: Risk assessment must come first to classify AI applications by explanation requirements before selecting techniques. CONTEXT: High-stakes decisions need rigorous explanation capability while lower-stakes applications may need less. REMEMBER: Start with risk assessment – know your stakes before choosing your approach. 2 / 7 2. Why is attention visualization for LLMs considered limited as an explanation technique? 1. Attention shows correlation not causation – high attention does not mean that input caused the output 2. Attention patterns cannot be visualized in real-time 3. Attention visualization only works for small language models 4. Attention mechanisms are not present in modern transformer 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. 3 / 7 3. 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. Deploy two models in different regions to meet local regulations 4. Use a high-performance model for predictions and a simpler model to generate explanations 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. 4 / 7 4. 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. 5 / 7 5. What is a counterfactual explanation? 1. An explanation that compares the current model to a previous version 2. An explanation that shows what would need to change for a different outcome 3. An explanation that predicts what the model would have decided in the past 4. An explanation that shows all factors that did not influence the decision Correct! WHY: Counterfactual explanations answer what would need to change for a different outcome making them actionable for users. CONTEXT: Users often find counterfactuals more useful than feature importance because they know what to do differently. REMEMBER: Counterfactuals explain by showing what you could change to get a different result. 6 / 7 6. What is the key limitation of post-hoc explanation methods like LIME and SHAP? 1. They require access to training data to function 2. They show correlations with model behavior not true internal reasoning 3. They only work with neural network models 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. 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 applies to inputs and explainability applies to model architecture 3. Transparency shows what goes into the system while explainability shows why a specific output was produced 4. Transparency is required by law while explainability is optional 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.
