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 was the key consequence of the UK A-Level Algorithm controversy in 2020? 1. The government increased funding for AI education 2. Students were required to retake all exams in person 3. The algorithm was proven to be mathematically correct but poorly communicated 4. The lack of transparency made the system indefensible leading to reversal and public trust damage Correct! WHY: The lack of transparency about how the model weighted factors like school history made the system indefensible to the public. CONTEXT: The resulting reversal cost millions and damaged public trust in algorithmic decision-making. REMEMBER: Opaque algorithms that cannot be explained cannot be defended when challenged. 2 / 7 2. According to the EU AI Act what level of transparency is required for high-risk AI systems? 1. Basic disclosure of the model type only 2. Extensive documentation and explanation capability 3. No transparency requirements for AI systems 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. 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 automatically generate GDPR-compliant documentation 4. They are approved by all regulatory bodies for high-risk applications 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 compares the current model to a previous version 2. An explanation that shows all factors that did not influence the decision 3. An explanation that predicts what the model would have decided in the past 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. 5 / 7 5. What is the key limitation of post-hoc explanation methods like LIME and SHAP? 1. They only work with neural network models 2. They require access to training data to function 3. They cannot be applied to production systems 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. 6 / 7 6. Why is the accuracy-explainability trade-off a genuine business challenge for managers? 1. Explainable models are always more expensive to develop and maintain 2. The most accurate models are often the least explainable creating tension between performance and interpretability 3. There is no trade-off because modern XAI techniques have solved this problem 4. Regulators require all models to have the same level of explainability regardless of accuracy 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 applies to inputs and explainability applies to model architecture 2. There is no meaningful difference – both terms mean the same thing 3. Transparency is required by law while explainability is optional 4. Transparency shows what goes into the system while explainability shows why a specific output was produced 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.
