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. Risk assessment to classify AI applications by explanation requirements 2. Documenting all existing model architectures 3. Purchasing LIME and SHAP software licenses 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. 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. Train two identical models and compare their outputs for consistency 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. 3 / 7 3. What is a counterfactual explanation? 1. An explanation that compares the current model to a previous version 2. An explanation that predicts what the model would have decided in the past 3. An explanation that shows all factors that did not influence the decision 4. An explanation that shows what would need to change for a different outcome 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. 4 / 7 4. 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 retrain the model on new data 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 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. 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. Decision tree node splits 4. Random sampling of input features 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. Regulators require all models to have the same level of explainability regardless of accuracy 2. There is no trade-off because modern XAI techniques have solved this problem 3. Explainable models are always more expensive to develop and maintain 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. SOC 2 Type II 2. GDPR Article 22 3. EU AI Act Article 1 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. 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.
