Explainable AI (XAI) Solved MCQs

  ✅ Explainable AI (XAI) – 50 Solved MCQs Basic Concepts of XAI (1–20) 1. What is Explainable AI (XAI)? A. AI that creates documents B....

 


Explainable AI (XAI) – 50 Solved MCQs


Basic Concepts of XAI (1–20)

1. What is Explainable AI (XAI)?
A. AI that creates documents
B. AI that explains how decisions are made
C. AI with hidden layers
D. AI used for entertainment
Correct Answer: B
Explanation: XAI refers to systems that make their decisions understandable to humans.


2. Why is explainability important in AI?
A. To reduce training time
B. To make predictions faster
C. To build trust, transparency, and accountability
D. To reduce cost
Correct Answer: C


3. XAI is especially critical in:
A. Games
B. Medical diagnosis, finance, and law
C. Sports predictions
D. Online shopping
Correct Answer: B


4. A black-box model is:
A. Fully interpretable
B. A system with no outputs
C. A model whose internal workings are hard to interpret
D. A model that cannot be trained
Correct Answer: C


5. An interpretable model is:
A. One that needs deep learning
B. Easy for machines to understand
C. Easily understood by humans
D. Hidden and encrypted
Correct Answer: C


6. Which of the following is NOT a goal of XAI?
A. Interpretability
B. Accuracy
C. Transparency
D. Deception
Correct Answer: D


7. Which type of model is more explainable by default?
A. Neural Networks
B. Decision Trees
C. Deep CNNs
D. Random Forest
Correct Answer: B


8. Post-hoc explanations refer to:
A. Explanations designed before model creation
B. Pre-training steps
C. Explanations generated after the model makes a prediction
D. Raw data interpretation
Correct Answer: C


9. What does LIME stand for?
A. Local Interpretable Model-agnostic Explanations
B. Learning in Machine Environments
C. Linear Interpolation for Model Explanations
D. Local Instance Mapping
Correct Answer: A


10. LIME is considered:
A. Global explanation technique
B. Local explanation technique
C. Rule-based model
D. Supervised model
Correct Answer: B


11. Which explanation technique is used to visualize neural networks’ decisions?
A. SVM
B. Decision rules
C. Saliency maps
D. KNN
Correct Answer: C


12. Which term describes whether a human can understand why a model made a certain prediction?
A. Generalization
B. Transparency
C. Interpretability
D. Optimization
Correct Answer: C


13. What does SHAP stand for?
A. Structured Heuristic Attribute Prediction
B. SHadow and Prediction
C. SHapley Additive exPlanations
D. Smart Heuristic Applied Prediction
Correct Answer: C


14. SHAP values are based on:
A. Random sampling
B. Game theory
C. Linear regression
D. Loss functions
Correct Answer: B


15. Which type of XAI technique uses decision rules to explain outcomes?
A. LIME
B. SHAP
C. Rule-based methods
D. CNN
Correct Answer: C


16. Counterfactual explanations answer the question:
A. What is the root cause?
B. What else could have led to a different outcome?
C. What data was missing?
D. How fast was the model?
Correct Answer: B


17. Which model is usually considered “white-box”?
A. Deep learning
B. Decision trees
C. GANs
D. Autoencoders
Correct Answer: B


18. Which of the following is a limitation of explainable AI?
A. Always accurate
B. May reduce model performance
C. Works only with text
D. Makes models smaller
Correct Answer: B


19. Trust in AI can be improved by:
A. High latency
B. Incomplete explanations
C. Clear and understandable model decisions
D. Obfuscating rules
Correct Answer: C


20. What is the main trade-off in XAI?
A. Between cost and accuracy
B. Between model complexity and interpretability
C. Between speed and memory
D. Between training and inference
Correct Answer: B


XAI Techniques & Tools (21–40)

21. Model-agnostic techniques:
A. Only work with CNNs
B. Require model internals
C. Work across various models
D. Are specific to KNN
Correct Answer: C


22. What is the purpose of saliency maps in CNNs?
A. To train the model
B. To visualize training loss
C. To highlight input regions important for the prediction
D. To reduce computation
Correct Answer: C


23. A global explanation explains:
A. One prediction
B. A random variable
C. Overall model behavior
D. A random sample
Correct Answer: C


24. Which of the following is NOT an XAI tool or library?
A. SHAP
B. LIME
C. TensorBoard
D. Pandas
Correct Answer: D


25. Feature importance techniques in XAI help to:
A. Speed up training
B. Select datasets
C. Identify which features contributed most to predictions
D. Perform normalization
Correct Answer: C


26. Explainable AI improves regulatory compliance by:
A. Hiding features
B. Allowing traceability of decisions
C. Encrypting models
D. Ignoring user feedback
Correct Answer: B


27. Local explanations are useful when:
A. You want to summarize entire model behavior
B. Explaining a single instance prediction
C. Optimizing datasets
D. Training multiple models
Correct Answer: B


28. Which sector has high demand for explainable AI due to legal constraints?
A. Gaming
B. Food delivery
C. Finance
D. Fitness apps
Correct Answer: C


29. Which technique visually explains how changes in input affect output?
A. SHAP
B. Counterfactuals
C. Attention maps
D. Data augmentation
Correct Answer: B


30. In SHAP, higher SHAP value means:
A. Feature is irrelevant
B. Feature negatively influences prediction
C. Greater contribution to prediction
D. Less weight during training
Correct Answer: C


31. Integrated gradients is a method for:
A. Feature scaling
B. Explaining neural networks
C. Model compression
D. Feature encoding
Correct Answer: B


32. In which stage is XAI most important?
A. After training (model evaluation)
B. During data collection
C. During model compilation
D. GPU configuration
Correct Answer: A


33. Which XAI tool is part of the Captum library?
A. LIME
B. SHAP
C. Integrated Gradients
D. ELI5
Correct Answer: C


34. XAI helps debug models by:
A. Cleaning code
B. Showing which features caused errors or bias
C. Compressing data
D. Improving RAM
Correct Answer: B


35. Which visualization tool is often used with PyTorch for XAI?
A. Matplotlib
B. Captum
C. Sklearn
D. OpenCV
Correct Answer: B


36. ELI5 is used for:
A. Explaining Linear Models and Tree models
B. Translating data
C. Label encoding
D. Cloud deployment
Correct Answer: A


37. Explainable models help AI become:
A. More secretive
B. More interpretable and socially acceptable
C. Less accurate
D. More expensive
Correct Answer: B


38. What is a “glass box” model?
A. Complex, deep model
B. Transparent model with interpretable internals
C. Model with no output
D. Audio-only model
Correct Answer: B


39. In sensitive domains, XAI helps detect:
A. Training speed
B. Bias, fairness, and safety issues
C. Hyperparameters
D. GPU types
Correct Answer: B


40. One key advantage of SHAP over LIME is:
A. Model-specific explanation
B. Use of deep learning
C. Consistency with global model behavior
D. Fast computation
Correct Answer: C


Ethics, Applications, and Challenges (41–50)

41. Which of the following is a major challenge in XAI?
A. Model training
B. Data visualization
C. Balancing explainability and accuracy
D. GPU compatibility
Correct Answer: C


42. Explainable AI can help prevent:
A. Overfitting
B. Ethical violations and bias
C. High resolution
D. Compilation errors
Correct Answer: B


43. GDPR regulations require that AI decisions:
A. Remain confidential
B. Be made quickly
C. Be explainable to the user
D. Be hidden from authorities
Correct Answer: C


44. A black-box attack in XAI refers to:
A. Model crashing
B. Reverse engineering the model
C. Random sampling
D. Data imbalance
Correct Answer: B


45. Which industry is NOT currently emphasizing XAI strongly?
A. Healthcare
B. Finance
C. Legal systems
D. Gaming
Correct Answer: D


46. XAI supports responsible AI by promoting:
A. Incomplete answers
B. Fairness, accountability, transparency
C. Black-box models
D. Closed-source tools
Correct Answer: B


47. Which ethical principle is closely tied to XAI?
A. Profit maximization
B. Transparency
C. Competition
D. Complexity
Correct Answer: B


48. What is “faithfulness” in XAI?
A. The model remains unchanged
B. The explanation accurately reflects the model's reasoning
C. The output is always correct
D. It doesn’t need training
Correct Answer: B


49. A faithful explanation is one that:
A. Uses complex math
B. Makes false assumptions
C. Matches the model’s actual logic
D. Is entertaining
Correct Answer: C


50. The ultimate goal of XAI is to:
A. Replace humans
B. Build complicated models
C. Make AI decisions understandable, fair, and trustworthy
D. Speed up processing
Correct Answer: C

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COMPUTER SCIENCE SOLVED MCQS: Explainable AI (XAI) Solved MCQs
Explainable AI (XAI) Solved MCQs
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COMPUTER SCIENCE SOLVED MCQS
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