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. Deploy two models in different regions to meet local regulations 2. Use one model during development and another during production 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. 2 / 7 2. A healthcare organization wants to deploy AI for patient triage. Which audience needs the simplest and most actionable explanations? 1. Developers who need to debug the model 2. End users who need to understand and act on decisions 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 achieve competitive accuracy while remaining fully interpretable by design 2. They require no training data to make predictions 3. They are approved by all regulatory bodies for high-risk applications 4. They automatically generate GDPR-compliant documentation 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 a counterfactual explanation? 1. An explanation that shows all factors that did not influence the decision 2. An explanation that predicts what the model would have decided in the past 3. An explanation that shows what would need to change for a different outcome 4. An explanation that compares the current model to a previous version 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. 5 / 7 5. What does SHAP use to determine feature importance in AI predictions? 1. Decision tree node splits 2. Neural network attention patterns 3. Game theory concepts specifically Shapley values 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. The most accurate models are often the least explainable creating tension between performance and interpretability 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. There is no trade-off because modern XAI techniques have solved this problem 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. NYC Local Law 144 2. SOC 2 Type II 3. EU AI Act Article 1 4. GDPR Article 22 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 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.
